5 Mallona et al. NAR 2020
Data from: https://doi.org/10.1093/nar/gkaa1168
5.0.1 initialize definitions
md_baubec <- tibble::tribble(
~name, ~track,
"active_3a", "Mallona_NAR_2020.HA36_TKO_DNMT3A2_r2",
"active_3b", "Mallona_NAR_2020.HA36_TKO_DNMT3B1_r2"
)
md_baubec## # A tibble: 2 x 2
## name track
## 1 active_3a Mallona_NAR_2020.HA36_TKO_DNMT3A2_r2
## 2 active_3b Mallona_NAR_2020.HA36_TKO_DNMT3B1_r2baubec_meth_all <- misha.ext::gextract_meth(tracks = md_baubec$track, names = md_baubec$name, extract_meth_calls = TRUE) %cache_df% here("output/baubec_meth_all.tsv") %>% as_tibble()baubec_meth_plus <- misha.ext::gextract_meth(tracks = paste0(md_baubec$track, "_plus"), names = md_baubec$name, extract_meth_calls = TRUE) %cache_df% here("output/baubec_meth_plus.tsv") %>% as_tibble()
baubec_meth_minus <- misha.ext::gextract_meth(tracks = paste0(md_baubec$track, "_minus"), names = md_baubec$name, extract_meth_calls = TRUE) %cache_df% here("output/baubec_meth_minus.tsv") %>% as_tibble()## [1] 16082106## [1] 12036497## [1] 12028654## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 10.0 11.0 14.0 16.5 18.0 1972.0## [1] "2,298,609"## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 10.00 11.00 14.00 16.36 18.00 1947.00## [1] "1,151,583"## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 10.00 11.00 14.00 16.58 18.00 1904.00## [1] "1,152,232"## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 4.000 4.291 6.000 190.000## [1] "15,928,534"## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 1.000 2.000 2.861 4.000 99.000## [1] "11,944,096"## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 1.000 2.000 2.864 4.000 101.000## [1] "11,936,048"baubec_meth_cov <- baubec_meth_all %>% filter(active_3a.cov >= 10, active_3b.cov >= 10)
nrow(baubec_meth_cov)## [1] 306664baubec_meth_plus_cov <- baubec_meth_plus %>% filter(active_3a.cov >= 10, active_3b.cov >= 10)
nrow(baubec_meth_plus_cov)## [1] 93967baubec_meth_minus_cov <- baubec_meth_minus %>% filter(active_3a.cov >= 10, active_3b.cov >= 10)
nrow(baubec_meth_minus_cov)## [1] 957475.1 Model ES
## Joining, by = c("chrom", "start", "end")## Joining, by = c("chrom", "start", "end")## [1] "48,354"## [1] "49,215"intervs_all_plus <- m_bau_plus %>% select(chrom, start, end)
intervs_all_minus <- m_bau_minus %>% select(chrom, start, end)seq_df_plus <- get_seq_df(intervs_all_plus, flank_bp = 5)
seq_df_minus <- get_seq_df(intervs_all_minus, strand = -1, flank_bp = 5)seq_df_wide_plus <- seq_df_to_wide(seq_df_plus, flank_bp = 5)
seq_df_wide_minus <- seq_df_to_wide(seq_df_minus, flank_bp = 5)## # A tibble: 6 x 11
## chrom start end active_3a active_3b active_3a.cov active_3b.cov
## 1 chr1 3322345 3322346 0.0000000 0.0 13 10
## 2 chr1 3574384 3574385 0.1764706 0.1 17 10
## 3 chr1 3574389 3574390 0.2352941 0.1 17 10
## 4 chr1 3574421 3574422 0.1904762 0.0 21 10
## 5 chr1 3698778 3698779 0.0000000 0.0 15 11
## 6 chr1 3708906 3708907 0.0000000 0.0 29 11
## active_3a.meth active_3b.meth intervalID esc
## 1 0 0 1 0.5714286
## 2 3 1 1 0.9000000
## 3 4 1 1 0.8333333
## 4 4 0 1 0.9285714
## 5 0 0 1 1.0000000
## 6 0 0 1 0.8333333model_ab_bau_plus_A <- gen_seq_model(seq_df_wide_plus, m_bau_plus, active_3a) %cache_rds% here("output/baubec_plus_a_dinuc_model_5bp.rds")
model_ab_bau_plus_B <- gen_seq_model(seq_df_wide_plus, m_bau_plus, active_3b) %cache_rds% here("output/baubec_plus_b_dinuc_model_5bp.rds")
model_ab_bau_minus_A <- gen_seq_model(seq_df_wide_minus, m_bau_minus, active_3a) %cache_rds% here("output/baubec_plus_a_dinuc_model_5bp.rds")
model_ab_bau_minus_B <- gen_seq_model(seq_df_wide_minus, m_bau_minus, active_3b) %cache_rds% here("output/baubec_plus_b_dinuc_model_5bp.rds")options(repr.plot.width = 12, repr.plot.height = 6)
bandwidth <- 0.08
point_size <- 0.001
p_a <- tibble(pred = model_ab_bau_plus_A$pred, y = model_ab_bau_plus_A$y) %>%
mutate(col = densCols(., bandwidth=0.06,colramp=colorRampPalette(c("white","lightblue", "blue", "darkblue", "yellow", "gold","orange","red", "darkred" )))) %>%
ggplot(aes(x=pred, y=y, col=col)) +
geom_point(shape=19, size=point_size) +
scale_color_identity() +
xlab("Dinucleotide combined model") +
ylab("Meth (3a-/-)") +
theme(aspect.ratio=1, panel.grid.major=element_blank(), panel.grid.minor=element_blank()) +
labs(subtitle = glue("R^2 = {cor}", cor = round(cor(model_ab_bau_plus_A$pred, model_ab_bau_plus_A$y)^2, digits=2))) +
theme(plot.subtitle = ggtext::element_markdown())
p_b <- tibble(pred = model_ab_bau_plus_B$pred, y = model_ab_bau_plus_B$y) %>%
mutate(col = densCols(., bandwidth=0.06,colramp=colorRampPalette(c("white","lightblue", "blue", "darkblue", "yellow", "gold","orange","red", "darkred" )))) %>%
ggplot(aes(x=pred, y=y, col=col)) +
geom_point(shape=19, size=point_size) +
scale_color_identity() +
xlab("Dinucleotide combined model") +
ylab("Meth (3b-/-)") +
theme(aspect.ratio=1, panel.grid.major=element_blank(), panel.grid.minor=element_blank()) +
labs(subtitle = glue("R^2 = {cor}", cor = round(cor(model_ab_bau_plus_B$pred, model_ab_bau_plus_B$y)^2, digits=2))) +
theme(plot.subtitle = ggtext::element_markdown())
p_a + p_b
## Loading required package: Matrix##
## Attaching package: 'Matrix'## The following objects are masked from 'package:tidyr':
##
## expand, pack, unpack## Loaded glmnet 4.1-45.1.1 Figure 5B,C
options(repr.plot.width = 12, repr.plot.height = 6)
p_a <- coef_df_A %>%
ggplot(aes(x=pos, y=dinuc, fill=coefficient)) +
geom_tile() +
scale_fill_gradient2(low = "darkblue", high = "darkred", mid = "white", midpoint = 0, na.value="white") +
theme_minimal() +
ylab("Dinucleotide") +
xlab("Position") +
ggtitle("A")
p_b <- coef_df_B %>%
ggplot(aes(x=pos, y=dinuc, fill=coefficient)) +
geom_tile() +
scale_fill_gradient2(low = "darkblue", high = "darkred", mid = "white", midpoint = 0, na.value="white") +
theme_minimal() +
ylab("Dinucleotide") +
xlab("Position") +
ggtitle("B")
p_a + p_b
model_ab <- readr::read_rds(here("output/ab_dinuc_model_5bp.rds"))
model_a <- readr::read_rds(here("output/a_dinuc_model_5bp.rds"))
model_b <- readr::read_rds(here("output/b_dinuc_model_5bp.rds"))intercept_A <- coef(model_ab_bau_plus_A$fit_cv, s = "lambda.min")[1, ]
intercept_B <- coef(model_ab_bau_plus_B$fit_cv, s = "lambda.min")[1, ]coef_mat_A <- coef_df_to_matrix(coef_df_A %>% select(pos, dinuc, coefficient), model_ab, intercept=intercept_A) %cache_rds% here("output/coef_A_mat.rds")
coef_mat_B <- coef_df_to_matrix(coef_df_B %>% select(pos, dinuc, coefficient), model_ab, intercept=intercept_B) %cache_rds% here("output/coef_B_mat.rds")5.1.2 Figure 5D
a_limits <- c(-0.1,0.5)
a_bins <- seq(-0.1, 0.5, length.out = 30)
b_limits <- c(-0.05,0.2)
b_bins <- seq(-0.05, 0.2, length.out = 30)cols <- colorRampPalette(c("white","lightblue", "blue", "darkblue", "yellow", "gold","orange","red", "darkred"))
options(repr.plot.width = 7, repr.plot.height = 7)
smoothScatter(
res$score_a_plus,
res$score_a_minus,
xlab="+",
ylab = "-",
xlim = a_limits,
ylim = a_limits,
colramp = cols)
abline(v = a_bins, h = a_bins, col=alpha(rgb(0,0,0), 0.1))
abline(a = 0, b = 1, lty=5)
5.1.3 Figure 5E
options(repr.plot.width = 7, repr.plot.height = 7)
smoothScatter(
res$score_b_plus,
res$score_b_minus,
xlab="+",
ylab = "-",
xlim = b_limits,
ylim = b_limits,
colramp = cols)
abline(v = b_bins, h = b_bins, col=alpha(rgb(0,0,0), 0.1))
abline(a = 0, b = 1, lty=5)
df <- {
df_wt <- tracks_key %>% filter(day == "d5") %>% filter(line == "wt")
df_ko3a <- tracks_key %>% filter(day == "d5") %>% filter(line == "ko3a")
df_ko3b <- tracks_key %>% filter(day == "d5") %>% filter(line == "ko3b")
df_ab_bulk <- extract_meth_annot(c(
df_wt$track_name,
df_ko3a$track_name,
df_ko3b$track_name),
names = c(df_wt$name, df_ko3a$name, df_ko3b$name), d_expand = 250, extract_meth_calls = TRUE)
df <- df_ab_bulk %>% select(chrom, start, end, d_exon:gc_cont)
df$wt.meth <- rowSums(df_ab_bulk[, paste0(df_wt$name, ".meth")], na.rm=TRUE)
df$wt.cov <- rowSums(df_ab_bulk[, paste0(df_wt$name, ".cov")], na.rm=TRUE)
df$ko3a.meth <- rowSums(df_ab_bulk[, paste0(df_ko3a$name, ".meth")], na.rm=TRUE)
df$ko3a.cov <- rowSums(df_ab_bulk[, paste0(df_ko3a$name, ".cov")], na.rm=TRUE)
df$ko3b.meth <- rowSums(df_ab_bulk[, paste0(df_ko3b$name, ".meth")], na.rm=TRUE)
df$ko3b.cov <- rowSums(df_ab_bulk[, paste0(df_ko3b$name, ".cov")], na.rm=TRUE)
df$ko3a <- df$ko3a.meth / df$ko3a.cov
df$wt <- df$wt.meth / df$wt.cov
df$ko3b <- df$ko3b.meth / df$ko3b.cov
df
} %cache_df% here("output/ab_diff_d5_bulk.tsv") %>% as_tibble()We take only regions that are well covered and add a diff column with 3a(-/-) - 3b(-/-)
min_cov <- 50
df_diff <- {
df_diff <- df %>% filter(ko3a.cov >= min_cov, ko3b.cov >= min_cov, wt.cov >= min_cov) %>% mutate(diff = ko3a - ko3b)
df_diff <- df_diff %>% gintervals.neighbors1("intervs.global.tss") %>% select(chrom:diff, geneSymbol, dist)
df_diff
} %cache_df% here("output/ab_diff_d5_bulk_diff.tsv") %>% as_tibble()## # A tibble: 6 x 23
## chrom start end d_exon d_tss tor ab_score a_score
## 1 chr1 3137716 3137717 58268 67996 -0.807965 -0.06375566 -0.0563031
## 2 chr1 3137749 3137750 58235 67963 -0.807965 0.35113215 0.1569280
## 3 chr1 3137829 3137830 58155 67883 -0.807965 -0.84893745 -0.5477075
## 4 chr1 3137834 3137835 58150 67878 -0.807965 -0.15073226 -0.1115560
## 5 chr1 3137836 3137837 58148 67876 -0.807965 -0.32485920 -0.2463233
## 6 chr1 3137840 3137841 58144 67872 -0.807965 -0.10740878 0.0732030
## b_score cg_cont gc_cont wt.meth wt.cov ko3a.meth ko3a.cov ko3b.meth
## 1 -0.07885952 0.03636364 0.5036364 84 90 88 95 50
## 2 -0.24173479 0.03636364 0.5036364 90 96 92 100 54
## 3 0.04839351 0.03272727 0.5054545 93 100 97 105 55
## 4 -0.05077374 0.03272727 0.5054545 93 100 97 105 55
## 5 0.07614822 0.03272727 0.5054545 93 100 97 105 55
## 6 -0.11832523 0.03272727 0.5054545 93 100 97 105 55
## ko3b.cov ko3a wt ko3b diff geneSymbol dist
## 1 58 0.9263158 0.9333333 0.8620690 0.06424682 mKIAA1889 67995
## 2 62 0.9200000 0.9375000 0.8709677 0.04903226 mKIAA1889 67962
## 3 61 0.9238095 0.9300000 0.9016393 0.02217018 mKIAA1889 67882
## 4 61 0.9238095 0.9300000 0.9016393 0.02217018 mKIAA1889 67877
## 5 61 0.9238095 0.9300000 0.9016393 0.02217018 mKIAA1889 67875
## 6 61 0.9238095 0.9300000 0.9016393 0.02217018 mKIAA1889 67871## Joining, by = c("chrom", "start", "end")meth_df_meeb_f <- meth_df_meeb %>%
filter(abs(d_tss) >= 2000) %>%
filter_low_esc_meth(esc_wgbs) %>%
gintervals.neighbors1(get_all_enhancers()) %>%
filter(dist != 0) %>%
select(-(chrom1:end1))## Joining, by = c("chrom", "start", "end")## [1] 947724 35## [1] "chrom" "start" "end"
## [4] "d_exon" "d_tss" "tor"
## [7] "ab_score" "a_score" "b_score"
## [10] "cg_cont" "gc_cont" "wt.meth"
## [13] "wt.cov" "ko3a.meth" "ko3a.cov"
## [16] "ko3b.meth" "ko3b.cov" "mA"
## [19] "wt" "mB" "diff"
## [22] "geneSymbol" "dist" "score_plus"
## [25] "score_minus" "score_a_plus" "score_a_minus"
## [28] "score_b_plus" "score_b_minus" "score_model_plus"
## [31] "score_model_minus" "score_orig_plus" "score_orig_minus"
## [34] "esc" "dist1"5.1.4 Figure 5F,G
meth_df_meeb_d4 <- calc_eb_day0_to_day4_cpg_meth(min_cov = 10, max_na = 5) %>%
rename(mA = d4_3a, mB = d4_3b) %>%
inner_join(fread(here("output/ebd_day1_to_day4_cpg_meth_mat.tsv")) %>% select(chrom, start, end)) %>%
left_join(res)## Joining, by = c("chrom", "start", "end")
## Joining, by = c("chrom", "start", "end")df_A <- meth_df_meeb_d4 %>%
mutate(score_plus = cut(score_a_plus, a_bins)) %>%
mutate(score_minus = cut(score_a_minus, a_bins)) %>%
group_by(score_plus, score_minus) %>%
summarise(mB = mean(mB, na.rm=TRUE)) %>%
filter(!is.na(score_plus), !is.na(score_minus)) %>%
tidyr::complete(fill=list(mB = NA)) %>%
arrange(score_plus) %>%
group_by(score_minus) %>%
mutate(mB = zoo::rollapply(mB, FUN = function(x) mean(x, na.rm=TRUE), width=4, fill=NA)) %>%
arrange(score_minus) %>%
group_by(score_plus) %>%
mutate(mB = zoo::rollapply(mB, FUN = function(x) mean(x, na.rm=TRUE), width=4, fill=NA)) %>%
na.omit() df_B <- meth_df_meeb_d4 %>%
mutate(score_plus = cut(score_b_plus, b_bins)) %>%
mutate(score_minus = cut(score_b_minus, b_bins)) %>%
group_by(score_plus, score_minus) %>%
summarise(mA = mean(mA, na.rm=TRUE)) %>%
filter(!is.na(score_plus), !is.na(score_minus)) %>%
tidyr::complete(fill=list(mA = NA)) %>%
arrange(score_plus) %>%
group_by(score_minus) %>%
mutate(mA = zoo::rollapply(mA, FUN = function(x) mean(x, na.rm=TRUE), width=4, fill=NA)) %>%
arrange(score_minus) %>%
group_by(score_plus) %>%
mutate(mA = zoo::rollapply(mA, FUN = function(x) mean(x, na.rm=TRUE), width=4, fill=NA)) %>%
na.omit()## [1] 0.4130196 0.9259376options(repr.plot.width = 7, repr.plot.height = 7)
colors <- viridis::viridis(30, option = "A")
p_A <- df_A %>%
ggplot(aes(x=score_plus, y=score_minus, fill=mB)) +
geom_tile() +
scale_fill_gradientn(colors=colors, limits=limits) +
vertical_labs() +
scale_x_discrete(drop=FALSE) +
scale_y_discrete(drop=FALSE) +
xlab("+ strand A model") +
ylab("- strand A model") +
ggtitle("MEEB 3B-/- methylation day 4") +
theme(axis.ticks.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.y = element_blank(),
axis.text.y = element_blank())
p_B <- df_B %>%
ggplot(aes(x=score_plus, y=score_minus, fill=mA)) +
geom_tile() +
scale_fill_gradientn(colors=colors, limits=limits) +
vertical_labs() +
scale_x_discrete(drop=FALSE) +
scale_y_discrete(drop=FALSE) +
xlab("+ strand B model") +
ylab("- strand B model") +
ggtitle("MEEB 3A-/- methylation Day 4") +
theme(axis.ticks.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.y = element_blank(),
axis.text.y = element_blank())
p_A 
5.1.4.1 Predict MEEB using strands
## Joining, by = c("chrom", "start", "end")feats_mat_comb_df <- meth_df_meeb %>%
inner_join(meth_df_meeb_d4 %>% select(chrom, start, end)) %>%
mutate(score_max_a = pmax(score_a_plus, score_a_minus)) %>%
mutate(score_max_b = pmax(score_b_plus, score_b_minus)) %>%
select(chrom, start, end, score_a_plus, score_a_minus, score_b_plus, score_b_minus, score_max_a, score_max_b, dAB) %>%
filter(!is.na(dAB))## Joining, by = c("chrom", "start", "end")## [1] 80228 7##
## Attaching package: 'xgboost'## The following object is masked from 'package:dplyr':
##
## slicedtrain <- xgb.DMatrix(feats_mat_comb[, -7], label=feats_mat_comb[, 7])
xgb_params <- readr::read_rds(here("data/xgb_params.rds"))
xgbcv <- xgb.cv(params = xgb_params$params, data = dtrain, nrounds = xgb_params$nrounds, nfold = 10, prediction = TRUE)## [1] train-rmse:0.914976+0.001127 test-rmse:0.914986+0.003794
## [2] train-rmse:0.881840+0.001363 test-rmse:0.881918+0.003766
## [3] train-rmse:0.851517+0.002020 test-rmse:0.851576+0.003881
## [4] train-rmse:0.823455+0.002133 test-rmse:0.823559+0.003889
## [5] train-rmse:0.796019+0.002401 test-rmse:0.796166+0.004076
## [6] train-rmse:0.770361+0.002147 test-rmse:0.770520+0.003819
## [7] train-rmse:0.746327+0.002155 test-rmse:0.746500+0.003163
## [8] train-rmse:0.724070+0.002124 test-rmse:0.724263+0.003047
## [9] train-rmse:0.702849+0.002098 test-rmse:0.703063+0.003522
## [10] train-rmse:0.683496+0.002147 test-rmse:0.683757+0.003792
## [11] train-rmse:0.665380+0.002060 test-rmse:0.665712+0.003957
## [12] train-rmse:0.648379+0.002221 test-rmse:0.648755+0.003472
## [13] train-rmse:0.632596+0.002191 test-rmse:0.633018+0.003580
## [14] train-rmse:0.617532+0.001933 test-rmse:0.618003+0.003785
## [15] train-rmse:0.603758+0.002239 test-rmse:0.604296+0.003706
## [16] train-rmse:0.590898+0.002381 test-rmse:0.591473+0.004014
## [17] train-rmse:0.579237+0.002366 test-rmse:0.579846+0.004148
## [18] train-rmse:0.568065+0.002676 test-rmse:0.568709+0.004518
## [19] train-rmse:0.557567+0.002579 test-rmse:0.558257+0.004369
## [20] train-rmse:0.547671+0.002278 test-rmse:0.548402+0.004297
## [21] train-rmse:0.538665+0.002351 test-rmse:0.539429+0.003962
## [22] train-rmse:0.530119+0.002005 test-rmse:0.530909+0.003751
## [23] train-rmse:0.522251+0.001838 test-rmse:0.523070+0.003556
## [24] train-rmse:0.514953+0.002109 test-rmse:0.515808+0.003846
## [25] train-rmse:0.508429+0.002010 test-rmse:0.509311+0.003520
## [26] train-rmse:0.502401+0.002193 test-rmse:0.503307+0.003477
## [27] train-rmse:0.496522+0.002050 test-rmse:0.497480+0.003282
## [28] train-rmse:0.490998+0.001981 test-rmse:0.492004+0.003126
## [29] train-rmse:0.486138+0.001793 test-rmse:0.487193+0.003178
## [30] train-rmse:0.481687+0.001782 test-rmse:0.482773+0.003209
## [31] train-rmse:0.477337+0.001614 test-rmse:0.478458+0.003045
## [32] train-rmse:0.473369+0.001467 test-rmse:0.474511+0.003157
## [33] train-rmse:0.469594+0.001314 test-rmse:0.470771+0.003266
## [34] train-rmse:0.466374+0.001308 test-rmse:0.467602+0.003311
## [35] train-rmse:0.463150+0.001319 test-rmse:0.464414+0.003150
## [36] train-rmse:0.460304+0.001251 test-rmse:0.461611+0.003117
## [37] train-rmse:0.457697+0.001240 test-rmse:0.459039+0.003059
## [38] train-rmse:0.455382+0.001271 test-rmse:0.456758+0.003097
## [39] train-rmse:0.453036+0.001213 test-rmse:0.454469+0.003120
## [40] train-rmse:0.450887+0.001087 test-rmse:0.452339+0.003149
## [41] train-rmse:0.448949+0.001209 test-rmse:0.450419+0.003057
## [42] train-rmse:0.447170+0.001304 test-rmse:0.448663+0.002932
## [43] train-rmse:0.445465+0.001358 test-rmse:0.446978+0.002960
## [44] train-rmse:0.443982+0.001341 test-rmse:0.445526+0.003019
## [45] train-rmse:0.442581+0.001324 test-rmse:0.444158+0.002924
## [46] train-rmse:0.441093+0.001186 test-rmse:0.442705+0.002965
## [47] train-rmse:0.439832+0.001164 test-rmse:0.441465+0.002948
## [48] train-rmse:0.438692+0.001049 test-rmse:0.440359+0.002932
## [49] train-rmse:0.437629+0.001122 test-rmse:0.439333+0.002960
## [50] train-rmse:0.436573+0.001122 test-rmse:0.438302+0.003003
## [51] train-rmse:0.435616+0.001061 test-rmse:0.437382+0.003023
## [52] train-rmse:0.434851+0.001051 test-rmse:0.436639+0.003046
## [53] train-rmse:0.434095+0.001005 test-rmse:0.435904+0.002975
## [54] train-rmse:0.433337+0.000960 test-rmse:0.435178+0.002938
## [55] train-rmse:0.432671+0.000949 test-rmse:0.434528+0.002990
## [56] train-rmse:0.432049+0.000952 test-rmse:0.433931+0.002964
## [57] train-rmse:0.431413+0.000852 test-rmse:0.433329+0.002994
## [58] train-rmse:0.430844+0.000782 test-rmse:0.432774+0.003021
## [59] train-rmse:0.430321+0.000795 test-rmse:0.432276+0.003034
## [60] train-rmse:0.429837+0.000757 test-rmse:0.431818+0.003016
## [61] train-rmse:0.429402+0.000702 test-rmse:0.431396+0.003049
## [62] train-rmse:0.429007+0.000697 test-rmse:0.431017+0.003045
## [63] train-rmse:0.428635+0.000721 test-rmse:0.430675+0.003021
## [64] train-rmse:0.428297+0.000743 test-rmse:0.430352+0.003026
## [65] train-rmse:0.427952+0.000754 test-rmse:0.430034+0.003034
## [66] train-rmse:0.427665+0.000772 test-rmse:0.429774+0.003017
## [67] train-rmse:0.427372+0.000781 test-rmse:0.429505+0.003029
## [68] train-rmse:0.427090+0.000772 test-rmse:0.429248+0.003045
## [69] train-rmse:0.426791+0.000724 test-rmse:0.428978+0.003049
## [70] train-rmse:0.426540+0.000684 test-rmse:0.428745+0.003084
## [71] train-rmse:0.426297+0.000671 test-rmse:0.428525+0.003105
## [72] train-rmse:0.426060+0.000660 test-rmse:0.428312+0.003116
## [73] train-rmse:0.425814+0.000614 test-rmse:0.428094+0.003154
## [74] train-rmse:0.425589+0.000615 test-rmse:0.427899+0.003131
## [75] train-rmse:0.425380+0.000589 test-rmse:0.427702+0.003141
## [76] train-rmse:0.425192+0.000576 test-rmse:0.427529+0.003150
## [77] train-rmse:0.425029+0.000558 test-rmse:0.427387+0.003159
## [78] train-rmse:0.424866+0.000556 test-rmse:0.427239+0.003182
## [79] train-rmse:0.424706+0.000547 test-rmse:0.427097+0.003194
## [80] train-rmse:0.424549+0.000558 test-rmse:0.426966+0.003194
## [81] train-rmse:0.424394+0.000564 test-rmse:0.426840+0.003205
## [82] train-rmse:0.424261+0.000561 test-rmse:0.426725+0.003196
## [83] train-rmse:0.424131+0.000571 test-rmse:0.426618+0.003195
## [84] train-rmse:0.424018+0.000566 test-rmse:0.426529+0.003202
## [85] train-rmse:0.423883+0.000541 test-rmse:0.426417+0.003201
## [86] train-rmse:0.423770+0.000532 test-rmse:0.426325+0.003217
## [87] train-rmse:0.423665+0.000538 test-rmse:0.426246+0.003222
## [88] train-rmse:0.423565+0.000540 test-rmse:0.426166+0.003215
## [89] train-rmse:0.423458+0.000537 test-rmse:0.426079+0.003214
## [90] train-rmse:0.423352+0.000525 test-rmse:0.425999+0.003230
## [91] train-rmse:0.423240+0.000506 test-rmse:0.425917+0.003244
## [92] train-rmse:0.423142+0.000494 test-rmse:0.425838+0.003245
## [93] train-rmse:0.423043+0.000475 test-rmse:0.425757+0.003257
## [94] train-rmse:0.422962+0.000465 test-rmse:0.425702+0.003263
## [95] train-rmse:0.422876+0.000462 test-rmse:0.425632+0.003264
## [96] train-rmse:0.422797+0.000456 test-rmse:0.425573+0.003270
## [97] train-rmse:0.422721+0.000459 test-rmse:0.425520+0.003267
## [98] train-rmse:0.422648+0.000455 test-rmse:0.425473+0.003273
## [99] train-rmse:0.422570+0.000448 test-rmse:0.425417+0.003274
## [100] train-rmse:0.422506+0.000441 test-rmse:0.425376+0.003284
## [101] train-rmse:0.422431+0.000435 test-rmse:0.425335+0.003281
## [102] train-rmse:0.422362+0.000432 test-rmse:0.425290+0.003289
## [103] train-rmse:0.422301+0.000439 test-rmse:0.425253+0.003283
## [104] train-rmse:0.422242+0.000441 test-rmse:0.425223+0.003278
## [105] train-rmse:0.422181+0.000436 test-rmse:0.425180+0.003283
## [106] train-rmse:0.422124+0.000435 test-rmse:0.425147+0.003285
## [107] train-rmse:0.422061+0.000433 test-rmse:0.425104+0.003286
## [108] train-rmse:0.422001+0.000430 test-rmse:0.425061+0.003291
## [109] train-rmse:0.421934+0.000432 test-rmse:0.425022+0.003294
## [110] train-rmse:0.421874+0.000424 test-rmse:0.424978+0.003305
## [111] train-rmse:0.421825+0.000422 test-rmse:0.424952+0.003310
## [112] train-rmse:0.421768+0.000420 test-rmse:0.424919+0.003313
## [113] train-rmse:0.421722+0.000416 test-rmse:0.424893+0.003323
## [114] train-rmse:0.421680+0.000417 test-rmse:0.424867+0.003320
## [115] train-rmse:0.421633+0.000413 test-rmse:0.424842+0.003323
## [116] train-rmse:0.421593+0.000414 test-rmse:0.424827+0.003331
## [117] train-rmse:0.421544+0.000409 test-rmse:0.424796+0.003332
## [118] train-rmse:0.421490+0.000410 test-rmse:0.424773+0.003334
## [119] train-rmse:0.421446+0.000408 test-rmse:0.424749+0.003336
## [120] train-rmse:0.421399+0.000408 test-rmse:0.424720+0.003334
## [121] train-rmse:0.421357+0.000407 test-rmse:0.424701+0.003339
## [122] train-rmse:0.421311+0.000404 test-rmse:0.424677+0.003335
## [123] train-rmse:0.421263+0.000407 test-rmse:0.424651+0.003337
## [124] train-rmse:0.421227+0.000408 test-rmse:0.424637+0.003340
## [125] train-rmse:0.421180+0.000406 test-rmse:0.424610+0.003341
## [126] train-rmse:0.421140+0.000401 test-rmse:0.424591+0.003349
## [127] train-rmse:0.421101+0.000399 test-rmse:0.424580+0.003353
## [128] train-rmse:0.421063+0.000399 test-rmse:0.424560+0.003353
## [129] train-rmse:0.421026+0.000399 test-rmse:0.424542+0.003354
## [130] train-rmse:0.420985+0.000399 test-rmse:0.424525+0.003359
## [131] train-rmse:0.420947+0.000397 test-rmse:0.424506+0.003362
## [132] train-rmse:0.420910+0.000394 test-rmse:0.424491+0.003366
## [133] train-rmse:0.420874+0.000393 test-rmse:0.424470+0.003371
## [134] train-rmse:0.420840+0.000393 test-rmse:0.424459+0.003372
## [135] train-rmse:0.420808+0.000388 test-rmse:0.424444+0.003377
## [136] train-rmse:0.420778+0.000386 test-rmse:0.424434+0.003381
## [137] train-rmse:0.420742+0.000383 test-rmse:0.424412+0.003387
## [138] train-rmse:0.420710+0.000384 test-rmse:0.424401+0.003388
## [139] train-rmse:0.420670+0.000377 test-rmse:0.424382+0.003393
## [140] train-rmse:0.420636+0.000374 test-rmse:0.424373+0.003400
## [141] train-rmse:0.420604+0.000378 test-rmse:0.424362+0.003397
## [142] train-rmse:0.420571+0.000377 test-rmse:0.424352+0.003397
## [143] train-rmse:0.420539+0.000378 test-rmse:0.424341+0.003397
## [144] train-rmse:0.420505+0.000381 test-rmse:0.424323+0.003397
## [145] train-rmse:0.420475+0.000380 test-rmse:0.424318+0.003398
## [146] train-rmse:0.420446+0.000381 test-rmse:0.424311+0.003395
## [147] train-rmse:0.420415+0.000377 test-rmse:0.424300+0.003394
## [148] train-rmse:0.420388+0.000379 test-rmse:0.424290+0.003397
## [149] train-rmse:0.420359+0.000377 test-rmse:0.424282+0.003403
## [150] train-rmse:0.420333+0.000376 test-rmse:0.424275+0.003403
## [151] train-rmse:0.420301+0.000378 test-rmse:0.424265+0.003400
## [152] train-rmse:0.420271+0.000378 test-rmse:0.424258+0.003400
## [153] train-rmse:0.420244+0.000375 test-rmse:0.424249+0.003407
## [154] train-rmse:0.420216+0.000370 test-rmse:0.424238+0.003415
## [155] train-rmse:0.420191+0.000368 test-rmse:0.424234+0.003419
## [156] train-rmse:0.420158+0.000368 test-rmse:0.424227+0.003419
## [157] train-rmse:0.420130+0.000368 test-rmse:0.424222+0.003424
## [158] train-rmse:0.420101+0.000371 test-rmse:0.424212+0.003424
## [159] train-rmse:0.420074+0.000374 test-rmse:0.424206+0.003424
## [160] train-rmse:0.420043+0.000376 test-rmse:0.424196+0.003424
## [161] train-rmse:0.420016+0.000377 test-rmse:0.424192+0.003422
## [162] train-rmse:0.419993+0.000377 test-rmse:0.424189+0.003423
## [163] train-rmse:0.419967+0.000376 test-rmse:0.424188+0.003420
## [164] train-rmse:0.419943+0.000375 test-rmse:0.424187+0.003422
## [165] train-rmse:0.419916+0.000374 test-rmse:0.424186+0.003425
## [166] train-rmse:0.419893+0.000375 test-rmse:0.424180+0.003427
## [167] train-rmse:0.419869+0.000374 test-rmse:0.424179+0.003431
## [168] train-rmse:0.419845+0.000371 test-rmse:0.424170+0.003436
## [169] train-rmse:0.419822+0.000369 test-rmse:0.424164+0.003442
## [170] train-rmse:0.419801+0.000372 test-rmse:0.424159+0.003441
## [171] train-rmse:0.419778+0.000376 test-rmse:0.424155+0.003434
## [172] train-rmse:0.419760+0.000372 test-rmse:0.424150+0.003435
## [173] train-rmse:0.419738+0.000370 test-rmse:0.424145+0.003437
## [174] train-rmse:0.419718+0.000368 test-rmse:0.424142+0.003436
## [175] train-rmse:0.419699+0.000365 test-rmse:0.424140+0.003437
## [176] train-rmse:0.419678+0.000364 test-rmse:0.424134+0.003437
## [177] train-rmse:0.419658+0.000367 test-rmse:0.424134+0.003438
## [178] train-rmse:0.419633+0.000366 test-rmse:0.424131+0.003435
## [179] train-rmse:0.419607+0.000365 test-rmse:0.424133+0.003438
## [180] train-rmse:0.419589+0.000364 test-rmse:0.424128+0.003439
## [181] train-rmse:0.419570+0.000362 test-rmse:0.424128+0.003440
## [182] train-rmse:0.419549+0.000359 test-rmse:0.424127+0.003443
## [183] train-rmse:0.419527+0.000360 test-rmse:0.424125+0.003443
## [184] train-rmse:0.419510+0.000360 test-rmse:0.424121+0.003448
## [185] train-rmse:0.419492+0.000360 test-rmse:0.424122+0.003450
## [186] train-rmse:0.419472+0.000358 test-rmse:0.424118+0.003449
## [187] train-rmse:0.419456+0.000355 test-rmse:0.424117+0.003450
## [188] train-rmse:0.419429+0.000360 test-rmse:0.424112+0.003447
## [189] train-rmse:0.419415+0.000359 test-rmse:0.424112+0.003448
## [190] train-rmse:0.419394+0.000357 test-rmse:0.424108+0.003447
## [191] train-rmse:0.419372+0.000360 test-rmse:0.424098+0.003446
## [192] train-rmse:0.419350+0.000364 test-rmse:0.424095+0.003445
## [193] train-rmse:0.419334+0.000368 test-rmse:0.424091+0.003444
## [194] train-rmse:0.419312+0.000373 test-rmse:0.424091+0.003445
## [195] train-rmse:0.419290+0.000375 test-rmse:0.424088+0.003443
## [196] train-rmse:0.419273+0.000374 test-rmse:0.424086+0.003446
## [197] train-rmse:0.419258+0.000378 test-rmse:0.424088+0.003445
## [198] train-rmse:0.419239+0.000376 test-rmse:0.424082+0.003449
## [199] train-rmse:0.419222+0.000375 test-rmse:0.424085+0.003452
## [200] train-rmse:0.419205+0.000374 test-rmse:0.424083+0.003454
## [201] train-rmse:0.419189+0.000371 test-rmse:0.424078+0.003454
## [202] train-rmse:0.419165+0.000364 test-rmse:0.424073+0.003457
## [203] train-rmse:0.419150+0.000362 test-rmse:0.424072+0.003462
## [204] train-rmse:0.419130+0.000367 test-rmse:0.424068+0.003460
## [205] train-rmse:0.419116+0.000364 test-rmse:0.424070+0.003459
## [206] train-rmse:0.419096+0.000365 test-rmse:0.424071+0.003459
## [207] train-rmse:0.419078+0.000364 test-rmse:0.424063+0.003462
## [208] train-rmse:0.419059+0.000362 test-rmse:0.424063+0.003464
## [209] train-rmse:0.419040+0.000366 test-rmse:0.424057+0.003467
## [210] train-rmse:0.419022+0.000363 test-rmse:0.424054+0.003467
## [211] train-rmse:0.419010+0.000365 test-rmse:0.424053+0.003466
## [212] train-rmse:0.418991+0.000370 test-rmse:0.424052+0.003465
## [213] train-rmse:0.418973+0.000370 test-rmse:0.424048+0.003466
## [214] train-rmse:0.418954+0.000371 test-rmse:0.424044+0.003465
## [215] train-rmse:0.418939+0.000372 test-rmse:0.424043+0.003463
## [216] train-rmse:0.418921+0.000370 test-rmse:0.424043+0.003464
## [217] train-rmse:0.418905+0.000364 test-rmse:0.424041+0.003465
## [218] train-rmse:0.418891+0.000369 test-rmse:0.424042+0.003463
## [219] train-rmse:0.418874+0.000369 test-rmse:0.424042+0.003469
## [220] train-rmse:0.418855+0.000371 test-rmse:0.424039+0.003471
## [221] train-rmse:0.418841+0.000371 test-rmse:0.424040+0.003471
## [222] train-rmse:0.418824+0.000371 test-rmse:0.424039+0.003472
## [223] train-rmse:0.418805+0.000372 test-rmse:0.424037+0.003468
## [224] train-rmse:0.418793+0.000373 test-rmse:0.424037+0.003469
## [225] train-rmse:0.418777+0.000375 test-rmse:0.424034+0.003471
## [226] train-rmse:0.418764+0.000378 test-rmse:0.424038+0.003468
## [227] train-rmse:0.418748+0.000380 test-rmse:0.424036+0.003465
## [228] train-rmse:0.418733+0.000384 test-rmse:0.424039+0.003464
## [229] train-rmse:0.418716+0.000386 test-rmse:0.424037+0.003463
## [230] train-rmse:0.418702+0.000391 test-rmse:0.424036+0.003462
## [231] train-rmse:0.418686+0.000390 test-rmse:0.424035+0.003464
## [232] train-rmse:0.418668+0.000387 test-rmse:0.424031+0.003464
## [233] train-rmse:0.418656+0.000385 test-rmse:0.424033+0.003466
## [234] train-rmse:0.418640+0.000384 test-rmse:0.424026+0.003466
## [235] train-rmse:0.418625+0.000385 test-rmse:0.424026+0.003468
## [236] train-rmse:0.418612+0.000387 test-rmse:0.424026+0.003468
## [237] train-rmse:0.418594+0.000384 test-rmse:0.424025+0.003468
## [238] train-rmse:0.418579+0.000381 test-rmse:0.424020+0.003472
## [239] train-rmse:0.418568+0.000383 test-rmse:0.424018+0.003470
## [240] train-rmse:0.418555+0.000384 test-rmse:0.424015+0.003468
## [241] train-rmse:0.418544+0.000384 test-rmse:0.424018+0.003471
## [242] train-rmse:0.418531+0.000385 test-rmse:0.424019+0.003472
## [243] train-rmse:0.418516+0.000385 test-rmse:0.424018+0.003474
## [244] train-rmse:0.418506+0.000386 test-rmse:0.424019+0.003474
## [245] train-rmse:0.418495+0.000387 test-rmse:0.424017+0.003475
## [246] train-rmse:0.418480+0.000386 test-rmse:0.424012+0.003475
## [247] train-rmse:0.418469+0.000384 test-rmse:0.424013+0.003475
## [248] train-rmse:0.418456+0.000383 test-rmse:0.424013+0.003478
## [249] train-rmse:0.418443+0.000382 test-rmse:0.424014+0.003481
## [250] train-rmse:0.418429+0.000384 test-rmse:0.424015+0.003481
## [251] train-rmse:0.418413+0.000385 test-rmse:0.424014+0.003483
## [252] train-rmse:0.418393+0.000381 test-rmse:0.424014+0.003483
## [253] train-rmse:0.418379+0.000380 test-rmse:0.424014+0.003485
## [254] train-rmse:0.418362+0.000379 test-rmse:0.424018+0.003488
## [255] train-rmse:0.418341+0.000376 test-rmse:0.424016+0.003491
## [256] train-rmse:0.418326+0.000375 test-rmse:0.424015+0.003491
## [257] train-rmse:0.418310+0.000380 test-rmse:0.424017+0.003491
## [258] train-rmse:0.418299+0.000380 test-rmse:0.424019+0.003491
## [259] train-rmse:0.418289+0.000377 test-rmse:0.424020+0.003490
## [260] train-rmse:0.418277+0.000379 test-rmse:0.424021+0.003492
## [261] train-rmse:0.418264+0.000379 test-rmse:0.424020+0.003494
## [262] train-rmse:0.418248+0.000377 test-rmse:0.424023+0.003496
## [263] train-rmse:0.418233+0.000379 test-rmse:0.424022+0.003498
## [264] train-rmse:0.418219+0.000376 test-rmse:0.424022+0.003498
## [265] train-rmse:0.418207+0.000378 test-rmse:0.424021+0.003499
## [266] train-rmse:0.418191+0.000378 test-rmse:0.424022+0.003495
## [267] train-rmse:0.418174+0.000382 test-rmse:0.424020+0.003492
## [268] train-rmse:0.418159+0.000383 test-rmse:0.424019+0.003489
## [269] train-rmse:0.418146+0.000381 test-rmse:0.424017+0.003492
## [270] train-rmse:0.418133+0.000379 test-rmse:0.424016+0.003492
## [271] train-rmse:0.418117+0.000380 test-rmse:0.424018+0.003490
## [272] train-rmse:0.418107+0.000379 test-rmse:0.424016+0.003490
## [273] train-rmse:0.418091+0.000380 test-rmse:0.424018+0.003493
## [274] train-rmse:0.418078+0.000376 test-rmse:0.424018+0.003494
## [275] train-rmse:0.418064+0.000374 test-rmse:0.424015+0.003497
## [276] train-rmse:0.418051+0.000378 test-rmse:0.424018+0.003497
## [277] train-rmse:0.418035+0.000375 test-rmse:0.424018+0.003499
## [278] train-rmse:0.418020+0.000376 test-rmse:0.424015+0.003501
## [279] train-rmse:0.418006+0.000377 test-rmse:0.424018+0.003501
## [280] train-rmse:0.417991+0.000375 test-rmse:0.424017+0.003503
## [281] train-rmse:0.417978+0.000372 test-rmse:0.424019+0.003503
## [282] train-rmse:0.417963+0.000372 test-rmse:0.424020+0.003505
## [283] train-rmse:0.417951+0.000370 test-rmse:0.424020+0.003505
## [284] train-rmse:0.417936+0.000374 test-rmse:0.424015+0.003508
## [285] train-rmse:0.417920+0.000374 test-rmse:0.424015+0.003509
## [286] train-rmse:0.417905+0.000375 test-rmse:0.424013+0.003506
## [287] train-rmse:0.417887+0.000373 test-rmse:0.424009+0.003508
## [288] train-rmse:0.417876+0.000372 test-rmse:0.424010+0.003506
## [289] train-rmse:0.417867+0.000372 test-rmse:0.424012+0.003506
## [290] train-rmse:0.417852+0.000373 test-rmse:0.424013+0.003506
## [291] train-rmse:0.417839+0.000372 test-rmse:0.424015+0.003508
## [292] train-rmse:0.417828+0.000373 test-rmse:0.424015+0.003508
## [293] train-rmse:0.417813+0.000374 test-rmse:0.424011+0.003508
## [294] train-rmse:0.417803+0.000373 test-rmse:0.424010+0.003509
## [295] train-rmse:0.417789+0.000372 test-rmse:0.424010+0.003510
## [296] train-rmse:0.417776+0.000367 test-rmse:0.424008+0.003510
## [297] train-rmse:0.417764+0.000368 test-rmse:0.424008+0.003510
## [298] train-rmse:0.417753+0.000366 test-rmse:0.424011+0.003508
## [299] train-rmse:0.417739+0.000363 test-rmse:0.424008+0.003512
## [300] train-rmse:0.417726+0.000358 test-rmse:0.424008+0.003511
## [301] train-rmse:0.417715+0.000360 test-rmse:0.424007+0.003513
## [302] train-rmse:0.417699+0.000363 test-rmse:0.424009+0.003512
## [303] train-rmse:0.417687+0.000364 test-rmse:0.424008+0.003512
## [304] train-rmse:0.417679+0.000363 test-rmse:0.424008+0.003513
## [305] train-rmse:0.417669+0.000364 test-rmse:0.424011+0.003515
## [306] train-rmse:0.417661+0.000366 test-rmse:0.424011+0.003515
## [307] train-rmse:0.417651+0.000363 test-rmse:0.424012+0.003517
## [308] train-rmse:0.417637+0.000365 test-rmse:0.424013+0.003518
## [309] train-rmse:0.417620+0.000366 test-rmse:0.424014+0.003515
## [310] train-rmse:0.417609+0.000363 test-rmse:0.424018+0.003513
## [311] train-rmse:0.417599+0.000366 test-rmse:0.424017+0.003515
## [312] train-rmse:0.417586+0.000368 test-rmse:0.424019+0.003518
## [313] train-rmse:0.417568+0.000372 test-rmse:0.424020+0.003515
## [314] train-rmse:0.417558+0.000374 test-rmse:0.424022+0.003517
## [315] train-rmse:0.417545+0.000370 test-rmse:0.424021+0.003516
## [316] train-rmse:0.417533+0.000369 test-rmse:0.424023+0.003517
## [317] train-rmse:0.417518+0.000374 test-rmse:0.424021+0.003515
## [318] train-rmse:0.417503+0.000376 test-rmse:0.424023+0.003514
## [319] train-rmse:0.417491+0.000378 test-rmse:0.424023+0.003514
## [320] train-rmse:0.417478+0.000378 test-rmse:0.424020+0.003511
## [321] train-rmse:0.417462+0.000380 test-rmse:0.424021+0.003511
## [322] train-rmse:0.417448+0.000383 test-rmse:0.424020+0.003513
## [323] train-rmse:0.417435+0.000383 test-rmse:0.424019+0.003513
## [324] train-rmse:0.417423+0.000385 test-rmse:0.424021+0.003512
## [325] train-rmse:0.417414+0.000387 test-rmse:0.424021+0.003516
## [326] train-rmse:0.417402+0.000389 test-rmse:0.424018+0.003517
## [327] train-rmse:0.417392+0.000390 test-rmse:0.424015+0.003517
## [328] train-rmse:0.417382+0.000390 test-rmse:0.424014+0.003516
## [329] train-rmse:0.417370+0.000392 test-rmse:0.424012+0.003514
## [330] train-rmse:0.417357+0.000393 test-rmse:0.424012+0.003514
## [331] train-rmse:0.417345+0.000397 test-rmse:0.424010+0.003513
## [332] train-rmse:0.417331+0.000396 test-rmse:0.424010+0.003512
## [333] train-rmse:0.417315+0.000396 test-rmse:0.424011+0.003513
## [334] train-rmse:0.417303+0.000396 test-rmse:0.424011+0.003515
## [335] train-rmse:0.417293+0.000393 test-rmse:0.424010+0.003514
## [336] train-rmse:0.417280+0.000389 test-rmse:0.424011+0.003511
## [337] train-rmse:0.417269+0.000383 test-rmse:0.424016+0.003510
## [338] train-rmse:0.417256+0.000380 test-rmse:0.424017+0.003513
## [339] train-rmse:0.417245+0.000384 test-rmse:0.424020+0.003513
## [340] train-rmse:0.417233+0.000381 test-rmse:0.424022+0.003514
## [341] train-rmse:0.417222+0.000384 test-rmse:0.424022+0.003517
## [342] train-rmse:0.417209+0.000382 test-rmse:0.424020+0.003519
## [343] train-rmse:0.417194+0.000383 test-rmse:0.424018+0.003520
## [344] train-rmse:0.417183+0.000383 test-rmse:0.424018+0.003520
## [345] train-rmse:0.417168+0.000377 test-rmse:0.424015+0.003523
## [346] train-rmse:0.417156+0.000374 test-rmse:0.424017+0.003522
## [347] train-rmse:0.417141+0.000377 test-rmse:0.424016+0.003522
## [348] train-rmse:0.417127+0.000379 test-rmse:0.424017+0.003519
## [349] train-rmse:0.417117+0.000380 test-rmse:0.424016+0.003518
## [350] train-rmse:0.417107+0.000379 test-rmse:0.424016+0.003520
## [351] train-rmse:0.417094+0.000379 test-rmse:0.424016+0.003521
## [352] train-rmse:0.417083+0.000382 test-rmse:0.424017+0.003524
## [353] train-rmse:0.417067+0.000379 test-rmse:0.424016+0.003521
## [354] train-rmse:0.417058+0.000377 test-rmse:0.424017+0.003520
## [355] train-rmse:0.417044+0.000380 test-rmse:0.424014+0.003523
## [356] train-rmse:0.417028+0.000383 test-rmse:0.424014+0.003527
## [357] train-rmse:0.417015+0.000378 test-rmse:0.424013+0.003530
## [358] train-rmse:0.417003+0.000377 test-rmse:0.424014+0.003530
## [359] train-rmse:0.416989+0.000379 test-rmse:0.424012+0.003528
## [360] train-rmse:0.416976+0.000385 test-rmse:0.424012+0.003528
## [361] train-rmse:0.416965+0.000386 test-rmse:0.424012+0.003529
## [362] train-rmse:0.416951+0.000389 test-rmse:0.424011+0.003529
## [363] train-rmse:0.416943+0.000387 test-rmse:0.424010+0.003531
## [364] train-rmse:0.416928+0.000384 test-rmse:0.424007+0.003533
## [365] train-rmse:0.416920+0.000380 test-rmse:0.424008+0.003536
## [366] train-rmse:0.416909+0.000377 test-rmse:0.424006+0.003536
## [367] train-rmse:0.416894+0.000375 test-rmse:0.424001+0.003538
## [368] train-rmse:0.416884+0.000374 test-rmse:0.424003+0.003535
## [369] train-rmse:0.416867+0.000373 test-rmse:0.424000+0.003538
## [370] train-rmse:0.416855+0.000371 test-rmse:0.424001+0.003538
## [371] train-rmse:0.416839+0.000371 test-rmse:0.424000+0.003537
## [372] train-rmse:0.416828+0.000370 test-rmse:0.424004+0.003537
## [373] train-rmse:0.416818+0.000368 test-rmse:0.424002+0.003536
## [374] train-rmse:0.416809+0.000363 test-rmse:0.424001+0.003537
## [375] train-rmse:0.416798+0.000357 test-rmse:0.424000+0.003538
## [376] train-rmse:0.416780+0.000359 test-rmse:0.424002+0.003537
## [377] train-rmse:0.416769+0.000361 test-rmse:0.423998+0.003538
## [378] train-rmse:0.416758+0.000360 test-rmse:0.423997+0.003540
## [379] train-rmse:0.416747+0.000360 test-rmse:0.423999+0.003542
## [380] train-rmse:0.416736+0.000364 test-rmse:0.423998+0.003540
## [381] train-rmse:0.416725+0.000367 test-rmse:0.424000+0.003539
## [382] train-rmse:0.416715+0.000368 test-rmse:0.424000+0.003542
## [383] train-rmse:0.416699+0.000367 test-rmse:0.423998+0.003544
## [384] train-rmse:0.416685+0.000365 test-rmse:0.423993+0.003546
## [385] train-rmse:0.416673+0.000365 test-rmse:0.423994+0.003546
## [386] train-rmse:0.416662+0.000366 test-rmse:0.423994+0.003544
## [387] train-rmse:0.416652+0.000367 test-rmse:0.423993+0.003545
## [388] train-rmse:0.416640+0.000366 test-rmse:0.423993+0.003547
## [389] train-rmse:0.416627+0.000362 test-rmse:0.423991+0.003550
## [390] train-rmse:0.416613+0.000362 test-rmse:0.423995+0.003547
## [391] train-rmse:0.416601+0.000360 test-rmse:0.423995+0.003549
## [392] train-rmse:0.416588+0.000357 test-rmse:0.423991+0.003550
## [393] train-rmse:0.416577+0.000356 test-rmse:0.423991+0.003552
## [394] train-rmse:0.416564+0.000356 test-rmse:0.423991+0.003554
## [395] train-rmse:0.416550+0.000356 test-rmse:0.423990+0.003557
## [396] train-rmse:0.416539+0.000359 test-rmse:0.423994+0.003555
## [397] train-rmse:0.416527+0.000357 test-rmse:0.423993+0.003557
## [398] train-rmse:0.416512+0.000353 test-rmse:0.423994+0.003558
## [399] train-rmse:0.416497+0.000350 test-rmse:0.423989+0.003561
## [400] train-rmse:0.416485+0.000350 test-rmse:0.423989+0.003563
## [401] train-rmse:0.416473+0.000349 test-rmse:0.423985+0.003566
## [402] train-rmse:0.416460+0.000347 test-rmse:0.423987+0.003569
## [403] train-rmse:0.416447+0.000347 test-rmse:0.423986+0.003569
## [404] train-rmse:0.416436+0.000349 test-rmse:0.423985+0.003567
## [405] train-rmse:0.416427+0.000349 test-rmse:0.423986+0.003567
## [406] train-rmse:0.416416+0.000350 test-rmse:0.423987+0.003568
## [407] train-rmse:0.416403+0.000353 test-rmse:0.423986+0.003571
## [408] train-rmse:0.416392+0.000352 test-rmse:0.423985+0.003571
## [409] train-rmse:0.416377+0.000348 test-rmse:0.423983+0.003573
## [410] train-rmse:0.416366+0.000348 test-rmse:0.423984+0.003573
## [411] train-rmse:0.416351+0.000348 test-rmse:0.423985+0.003574
## [412] train-rmse:0.416340+0.000345 test-rmse:0.423987+0.003573
## [413] train-rmse:0.416327+0.000341 test-rmse:0.423989+0.003573
## [414] train-rmse:0.416314+0.000341 test-rmse:0.423992+0.003573
## [415] train-rmse:0.416303+0.000338 test-rmse:0.423990+0.003576
## [416] train-rmse:0.416284+0.000334 test-rmse:0.423989+0.003577
## [417] train-rmse:0.416273+0.000332 test-rmse:0.423986+0.003579
## [418] train-rmse:0.416261+0.000332 test-rmse:0.423985+0.003577
## [419] train-rmse:0.416251+0.000331 test-rmse:0.423984+0.003578
## [420] train-rmse:0.416238+0.000329 test-rmse:0.423984+0.003575
## [421] train-rmse:0.416228+0.000334 test-rmse:0.423985+0.003576
## [422] train-rmse:0.416215+0.000337 test-rmse:0.423986+0.003577
## [423] train-rmse:0.416203+0.000337 test-rmse:0.423986+0.003573
## [424] train-rmse:0.416195+0.000338 test-rmse:0.423986+0.003573
## [425] train-rmse:0.416182+0.000341 test-rmse:0.423984+0.003572
## [426] train-rmse:0.416168+0.000343 test-rmse:0.423984+0.003574
## [427] train-rmse:0.416157+0.000343 test-rmse:0.423985+0.003577
## [428] train-rmse:0.416148+0.000343 test-rmse:0.423984+0.003577
## [429] train-rmse:0.416137+0.000346 test-rmse:0.423985+0.003577
## [430] train-rmse:0.416121+0.000343 test-rmse:0.423985+0.003578
## [431] train-rmse:0.416108+0.000343 test-rmse:0.423985+0.003575
## [432] train-rmse:0.416098+0.000348 test-rmse:0.423988+0.003575
## [433] train-rmse:0.416084+0.000351 test-rmse:0.423984+0.003569
## [434] train-rmse:0.416071+0.000355 test-rmse:0.423984+0.003572
## [435] train-rmse:0.416055+0.000353 test-rmse:0.423981+0.003575
## [436] train-rmse:0.416044+0.000354 test-rmse:0.423983+0.003576
## [437] train-rmse:0.416030+0.000350 test-rmse:0.423981+0.003577
## [438] train-rmse:0.416020+0.000354 test-rmse:0.423984+0.003575
## [439] train-rmse:0.416008+0.000354 test-rmse:0.423985+0.003574
## [440] train-rmse:0.415995+0.000354 test-rmse:0.423981+0.003575
## [441] train-rmse:0.415984+0.000352 test-rmse:0.423980+0.003576
## [442] train-rmse:0.415974+0.000350 test-rmse:0.423981+0.003575
## [443] train-rmse:0.415960+0.000349 test-rmse:0.423981+0.003575
## [444] train-rmse:0.415951+0.000349 test-rmse:0.423980+0.003578
## [445] train-rmse:0.415934+0.000353 test-rmse:0.423983+0.003579
## [446] train-rmse:0.415926+0.000355 test-rmse:0.423984+0.003578
## [447] train-rmse:0.415911+0.000357 test-rmse:0.423985+0.003577
## [448] train-rmse:0.415897+0.000358 test-rmse:0.423986+0.003575
## [449] train-rmse:0.415884+0.000360 test-rmse:0.423989+0.003573
## [450] train-rmse:0.415870+0.000358 test-rmse:0.423989+0.003571
## [451] train-rmse:0.415857+0.000360 test-rmse:0.423989+0.003571
## [452] train-rmse:0.415842+0.000363 test-rmse:0.423990+0.003573
## [453] train-rmse:0.415831+0.000363 test-rmse:0.423992+0.003574
## [454] train-rmse:0.415817+0.000361 test-rmse:0.423992+0.003576
## [455] train-rmse:0.415806+0.000360 test-rmse:0.423993+0.003578
## [456] train-rmse:0.415788+0.000361 test-rmse:0.423991+0.003578
## [457] train-rmse:0.415774+0.000362 test-rmse:0.423990+0.003578
## [458] train-rmse:0.415764+0.000366 test-rmse:0.423990+0.003581
## [459] train-rmse:0.415755+0.000362 test-rmse:0.423988+0.003583
## [460] train-rmse:0.415740+0.000366 test-rmse:0.423989+0.003578
## [461] train-rmse:0.415729+0.000362 test-rmse:0.423989+0.003579
## [462] train-rmse:0.415714+0.000361 test-rmse:0.423988+0.003580
## [463] train-rmse:0.415703+0.000362 test-rmse:0.423985+0.003579
## [464] train-rmse:0.415687+0.000360 test-rmse:0.423979+0.003575
## [465] train-rmse:0.415678+0.000361 test-rmse:0.423980+0.003572
## [466] train-rmse:0.415666+0.000360 test-rmse:0.423978+0.003571
## [467] train-rmse:0.415654+0.000358 test-rmse:0.423978+0.003572
## [468] train-rmse:0.415646+0.000357 test-rmse:0.423980+0.003573
## [469] train-rmse:0.415630+0.000359 test-rmse:0.423980+0.003574
## [470] train-rmse:0.415618+0.000357 test-rmse:0.423979+0.003574
## [471] train-rmse:0.415610+0.000354 test-rmse:0.423979+0.003576
## [472] train-rmse:0.415597+0.000352 test-rmse:0.423979+0.003580
## [473] train-rmse:0.415585+0.000350 test-rmse:0.423978+0.003580
## [474] train-rmse:0.415574+0.000348 test-rmse:0.423979+0.003580
## [475] train-rmse:0.415561+0.000344 test-rmse:0.423976+0.003581
## [476] train-rmse:0.415542+0.000344 test-rmse:0.423978+0.003580
## [477] train-rmse:0.415526+0.000343 test-rmse:0.423977+0.003583
## [478] train-rmse:0.415515+0.000340 test-rmse:0.423978+0.003582
## [479] train-rmse:0.415506+0.000341 test-rmse:0.423978+0.003581
## [480] train-rmse:0.415495+0.000338 test-rmse:0.423980+0.003583
## [481] train-rmse:0.415485+0.000339 test-rmse:0.423981+0.003581
## [482] train-rmse:0.415471+0.000339 test-rmse:0.423980+0.003581
## [483] train-rmse:0.415459+0.000341 test-rmse:0.423981+0.003581
## [484] train-rmse:0.415446+0.000338 test-rmse:0.423983+0.003576
## [485] train-rmse:0.415432+0.000339 test-rmse:0.423978+0.003576
## [486] train-rmse:0.415420+0.000339 test-rmse:0.423979+0.003578
## [487] train-rmse:0.415406+0.000340 test-rmse:0.423978+0.003576
## [488] train-rmse:0.415398+0.000339 test-rmse:0.423979+0.003578
## [489] train-rmse:0.415384+0.000338 test-rmse:0.423983+0.003580
## [490] train-rmse:0.415375+0.000339 test-rmse:0.423985+0.003578
## [491] train-rmse:0.415357+0.000336 test-rmse:0.423983+0.003581
## [492] train-rmse:0.415347+0.000337 test-rmse:0.423981+0.003580
## [493] train-rmse:0.415333+0.000338 test-rmse:0.423981+0.003578
## [494] train-rmse:0.415321+0.000339 test-rmse:0.423978+0.003576
## [495] train-rmse:0.415308+0.000338 test-rmse:0.423976+0.003575
## [496] train-rmse:0.415292+0.000337 test-rmse:0.423979+0.003577
## [497] train-rmse:0.415278+0.000338 test-rmse:0.423979+0.003577
## [498] train-rmse:0.415269+0.000339 test-rmse:0.423978+0.003577
## [499] train-rmse:0.415261+0.000339 test-rmse:0.423978+0.003578
## [500] train-rmse:0.415247+0.000338 test-rmse:0.423981+0.003581
## [501] train-rmse:0.415236+0.000341 test-rmse:0.423980+0.003583
## [502] train-rmse:0.415223+0.000341 test-rmse:0.423983+0.003584
## [503] train-rmse:0.415215+0.000340 test-rmse:0.423985+0.003585
## [504] train-rmse:0.415205+0.000341 test-rmse:0.423985+0.003585
## [505] train-rmse:0.415189+0.000341 test-rmse:0.423985+0.003585
## [506] train-rmse:0.415175+0.000342 test-rmse:0.423984+0.003586
## [507] train-rmse:0.415164+0.000341 test-rmse:0.423985+0.003587
## [508] train-rmse:0.415153+0.000338 test-rmse:0.423984+0.003590
## [509] train-rmse:0.415142+0.000337 test-rmse:0.423982+0.003588
## [510] train-rmse:0.415129+0.000339 test-rmse:0.423984+0.003590
## [511] train-rmse:0.415118+0.000339 test-rmse:0.423983+0.003588
## [512] train-rmse:0.415108+0.000338 test-rmse:0.423980+0.003589
## [513] train-rmse:0.415092+0.000342 test-rmse:0.423978+0.003585
## [514] train-rmse:0.415079+0.000345 test-rmse:0.423978+0.003586
## [515] train-rmse:0.415066+0.000346 test-rmse:0.423977+0.003586
## [516] train-rmse:0.415054+0.000344 test-rmse:0.423974+0.003588
## [517] train-rmse:0.415041+0.000344 test-rmse:0.423977+0.003587
## [518] train-rmse:0.415029+0.000344 test-rmse:0.423980+0.003586
## [519] train-rmse:0.415014+0.000344 test-rmse:0.423984+0.003584
## [520] train-rmse:0.415004+0.000343 test-rmse:0.423984+0.003582
## [521] train-rmse:0.414991+0.000344 test-rmse:0.423986+0.003580
## [522] train-rmse:0.414978+0.000343 test-rmse:0.423983+0.003581
## [523] train-rmse:0.414966+0.000345 test-rmse:0.423983+0.003581
## [524] train-rmse:0.414953+0.000341 test-rmse:0.423984+0.003584
## [525] train-rmse:0.414942+0.000337 test-rmse:0.423982+0.003585
## [526] train-rmse:0.414927+0.000335 test-rmse:0.423980+0.003585
## [527] train-rmse:0.414910+0.000339 test-rmse:0.423978+0.003586
## [528] train-rmse:0.414892+0.000338 test-rmse:0.423975+0.003588
## [529] train-rmse:0.414876+0.000338 test-rmse:0.423971+0.003587
## [530] train-rmse:0.414867+0.000335 test-rmse:0.423971+0.003587
## [531] train-rmse:0.414858+0.000337 test-rmse:0.423970+0.003588
## [532] train-rmse:0.414846+0.000335 test-rmse:0.423971+0.003590
## [533] train-rmse:0.414833+0.000333 test-rmse:0.423973+0.003592
## [534] train-rmse:0.414819+0.000334 test-rmse:0.423974+0.003590
## [535] train-rmse:0.414807+0.000329 test-rmse:0.423972+0.003593
## [536] train-rmse:0.414794+0.000330 test-rmse:0.423968+0.003590
## [537] train-rmse:0.414780+0.000333 test-rmse:0.423968+0.003591
## [538] train-rmse:0.414769+0.000332 test-rmse:0.423970+0.003588
## [539] train-rmse:0.414756+0.000338 test-rmse:0.423966+0.003585
## [540] train-rmse:0.414744+0.000339 test-rmse:0.423966+0.003584
## [541] train-rmse:0.414735+0.000343 test-rmse:0.423967+0.003586
## [542] train-rmse:0.414724+0.000343 test-rmse:0.423970+0.003586
## [543] train-rmse:0.414714+0.000343 test-rmse:0.423970+0.003588
## [544] train-rmse:0.414702+0.000342 test-rmse:0.423969+0.003590
## [545] train-rmse:0.414691+0.000342 test-rmse:0.423969+0.003591
## [546] train-rmse:0.414680+0.000343 test-rmse:0.423969+0.003592
## [547] train-rmse:0.414669+0.000342 test-rmse:0.423968+0.003594
## [548] train-rmse:0.414659+0.000338 test-rmse:0.423968+0.003596
## [549] train-rmse:0.414643+0.000335 test-rmse:0.423968+0.003596
## [550] train-rmse:0.414630+0.000330 test-rmse:0.423968+0.003595
## [551] train-rmse:0.414619+0.000327 test-rmse:0.423967+0.003597
## [552] train-rmse:0.414607+0.000328 test-rmse:0.423966+0.003597
## [553] train-rmse:0.414595+0.000330 test-rmse:0.423967+0.003595
## [554] train-rmse:0.414582+0.000332 test-rmse:0.423967+0.003594
## [555] train-rmse:0.414573+0.000334 test-rmse:0.423967+0.003596
## [556] train-rmse:0.414562+0.000336 test-rmse:0.423967+0.003595
## [557] train-rmse:0.414550+0.000339 test-rmse:0.423969+0.003598
## [558] train-rmse:0.414536+0.000339 test-rmse:0.423972+0.003596
## [559] train-rmse:0.414518+0.000340 test-rmse:0.423970+0.003595
## [560] train-rmse:0.414510+0.000341 test-rmse:0.423972+0.003597
## [561] train-rmse:0.414496+0.000339 test-rmse:0.423970+0.003596
## [562] train-rmse:0.414483+0.000340 test-rmse:0.423970+0.003598
## [563] train-rmse:0.414468+0.000341 test-rmse:0.423972+0.003595
## [564] train-rmse:0.414454+0.000343 test-rmse:0.423971+0.003594
## [565] train-rmse:0.414443+0.000343 test-rmse:0.423972+0.003594
## [566] train-rmse:0.414431+0.000342 test-rmse:0.423971+0.003594
## [567] train-rmse:0.414419+0.000344 test-rmse:0.423969+0.003597
## [568] train-rmse:0.414411+0.000346 test-rmse:0.423968+0.003597
## [569] train-rmse:0.414396+0.000349 test-rmse:0.423967+0.003595
## [570] train-rmse:0.414383+0.000349 test-rmse:0.423964+0.003593
## [571] train-rmse:0.414369+0.000348 test-rmse:0.423965+0.003594
## [572] train-rmse:0.414357+0.000351 test-rmse:0.423965+0.003595
## [573] train-rmse:0.414346+0.000350 test-rmse:0.423967+0.003598
## [574] train-rmse:0.414333+0.000356 test-rmse:0.423967+0.003594
## [575] train-rmse:0.414322+0.000357 test-rmse:0.423969+0.003595
## [576] train-rmse:0.414308+0.000360 test-rmse:0.423968+0.003594
## [577] train-rmse:0.414297+0.000358 test-rmse:0.423969+0.003597
## [578] train-rmse:0.414282+0.000363 test-rmse:0.423966+0.003596
## [579] train-rmse:0.414270+0.000366 test-rmse:0.423964+0.003593
## [580] train-rmse:0.414255+0.000364 test-rmse:0.423960+0.003595
## [581] train-rmse:0.414245+0.000364 test-rmse:0.423960+0.003595
## [582] train-rmse:0.414231+0.000362 test-rmse:0.423954+0.003595
## [583] train-rmse:0.414219+0.000366 test-rmse:0.423952+0.003593
## [584] train-rmse:0.414205+0.000363 test-rmse:0.423953+0.003593
## [585] train-rmse:0.414193+0.000358 test-rmse:0.423952+0.003599
## [586] train-rmse:0.414177+0.000354 test-rmse:0.423951+0.003599
## [587] train-rmse:0.414163+0.000354 test-rmse:0.423955+0.003600
## [588] train-rmse:0.414148+0.000354 test-rmse:0.423951+0.003602
## [589] train-rmse:0.414134+0.000353 test-rmse:0.423953+0.003601
## [590] train-rmse:0.414122+0.000349 test-rmse:0.423952+0.003602
## [591] train-rmse:0.414110+0.000349 test-rmse:0.423950+0.003600
## [592] train-rmse:0.414097+0.000351 test-rmse:0.423949+0.003601
## [593] train-rmse:0.414085+0.000350 test-rmse:0.423951+0.003600
## [594] train-rmse:0.414073+0.000347 test-rmse:0.423953+0.003598
## [595] train-rmse:0.414063+0.000352 test-rmse:0.423953+0.003598
## [596] train-rmse:0.414051+0.000358 test-rmse:0.423951+0.003598
## [597] train-rmse:0.414038+0.000357 test-rmse:0.423954+0.003601
## [598] train-rmse:0.414026+0.000358 test-rmse:0.423955+0.003601
## [599] train-rmse:0.414014+0.000358 test-rmse:0.423955+0.003601
## [600] train-rmse:0.414001+0.000360 test-rmse:0.423956+0.003605
## [601] train-rmse:0.413989+0.000367 test-rmse:0.423959+0.003609
## [602] train-rmse:0.413973+0.000366 test-rmse:0.423960+0.003605
## [603] train-rmse:0.413962+0.000370 test-rmse:0.423962+0.003601
## [604] train-rmse:0.413950+0.000368 test-rmse:0.423965+0.003604
## [605] train-rmse:0.413937+0.000366 test-rmse:0.423965+0.003603
## [606] train-rmse:0.413924+0.000363 test-rmse:0.423963+0.003604
## [607] train-rmse:0.413914+0.000364 test-rmse:0.423967+0.003606
## [608] train-rmse:0.413902+0.000366 test-rmse:0.423969+0.003604
## [609] train-rmse:0.413889+0.000369 test-rmse:0.423973+0.003606
## [610] train-rmse:0.413880+0.000371 test-rmse:0.423973+0.003607
## [611] train-rmse:0.413868+0.000370 test-rmse:0.423973+0.003610
## [612] train-rmse:0.413854+0.000370 test-rmse:0.423971+0.003611
## [613] train-rmse:0.413843+0.000370 test-rmse:0.423973+0.003609
## [614] train-rmse:0.413832+0.000374 test-rmse:0.423971+0.003609
## [615] train-rmse:0.413817+0.000371 test-rmse:0.423967+0.003610
## [616] train-rmse:0.413807+0.000372 test-rmse:0.423970+0.003611
## [617] train-rmse:0.413797+0.000374 test-rmse:0.423970+0.003610
## [618] train-rmse:0.413789+0.000373 test-rmse:0.423970+0.003609
## [619] train-rmse:0.413779+0.000376 test-rmse:0.423973+0.003612
## [620] train-rmse:0.413764+0.000377 test-rmse:0.423974+0.003617
## [621] train-rmse:0.413753+0.000378 test-rmse:0.423975+0.003616
## [622] train-rmse:0.413741+0.000377 test-rmse:0.423976+0.003617
## [623] train-rmse:0.413727+0.000377 test-rmse:0.423980+0.003616
## [624] train-rmse:0.413715+0.000375 test-rmse:0.423978+0.003615
## [625] train-rmse:0.413705+0.000374 test-rmse:0.423978+0.003617
## [626] train-rmse:0.413692+0.000373 test-rmse:0.423979+0.003616
## [627] train-rmse:0.413682+0.000371 test-rmse:0.423978+0.003618
## [628] train-rmse:0.413670+0.000370 test-rmse:0.423979+0.003616
## [629] train-rmse:0.413654+0.000369 test-rmse:0.423980+0.003617
## [630] train-rmse:0.413639+0.000370 test-rmse:0.423981+0.003617
## [631] train-rmse:0.413630+0.000371 test-rmse:0.423985+0.003619
## [632] train-rmse:0.413613+0.000369 test-rmse:0.423984+0.003621
## [633] train-rmse:0.413595+0.000369 test-rmse:0.423982+0.003618
## [634] train-rmse:0.413583+0.000374 test-rmse:0.423983+0.003619
## [635] train-rmse:0.413570+0.000371 test-rmse:0.423984+0.003620
## [636] train-rmse:0.413558+0.000374 test-rmse:0.423982+0.003618
## [637] train-rmse:0.413547+0.000371 test-rmse:0.423981+0.003622
## [638] train-rmse:0.413533+0.000369 test-rmse:0.423981+0.003622
## [639] train-rmse:0.413520+0.000371 test-rmse:0.423979+0.003618
## [640] train-rmse:0.413508+0.000371 test-rmse:0.423978+0.003618
## [641] train-rmse:0.413497+0.000373 test-rmse:0.423978+0.003618
## [642] train-rmse:0.413483+0.000375 test-rmse:0.423976+0.003617
## [643] train-rmse:0.413470+0.000377 test-rmse:0.423976+0.003618
## [644] train-rmse:0.413457+0.000378 test-rmse:0.423974+0.003617
## [645] train-rmse:0.413446+0.000377 test-rmse:0.423975+0.003619
## [646] train-rmse:0.413434+0.000379 test-rmse:0.423974+0.003618
## [647] train-rmse:0.413423+0.000377 test-rmse:0.423974+0.003619
## [648] train-rmse:0.413413+0.000378 test-rmse:0.423975+0.003620
## [649] train-rmse:0.413405+0.000377 test-rmse:0.423976+0.003616
## [650] train-rmse:0.413393+0.000377 test-rmse:0.423977+0.003616
## [651] train-rmse:0.413378+0.000376 test-rmse:0.423976+0.003615
## [652] train-rmse:0.413370+0.000377 test-rmse:0.423973+0.003616
## [653] train-rmse:0.413359+0.000375 test-rmse:0.423973+0.003616
## [654] train-rmse:0.413347+0.000379 test-rmse:0.423973+0.003614
## [655] train-rmse:0.413333+0.000378 test-rmse:0.423975+0.003616
## [656] train-rmse:0.413320+0.000381 test-rmse:0.423971+0.003613
## [657] train-rmse:0.413303+0.000379 test-rmse:0.423971+0.003613
## [658] train-rmse:0.413294+0.000382 test-rmse:0.423971+0.003613
## [659] train-rmse:0.413283+0.000385 test-rmse:0.423973+0.003615
## [660] train-rmse:0.413272+0.000387 test-rmse:0.423972+0.003617
## [661] train-rmse:0.413261+0.000386 test-rmse:0.423973+0.003618
## [662] train-rmse:0.413251+0.000386 test-rmse:0.423973+0.003620
## [663] train-rmse:0.413236+0.000388 test-rmse:0.423974+0.003617
## [664] train-rmse:0.413226+0.000389 test-rmse:0.423975+0.003619
## [665] train-rmse:0.413212+0.000388 test-rmse:0.423978+0.003618
## [666] train-rmse:0.413201+0.000388 test-rmse:0.423979+0.003620
## [667] train-rmse:0.413188+0.000386 test-rmse:0.423978+0.003621
## [668] train-rmse:0.413176+0.000384 test-rmse:0.423975+0.003620
## [669] train-rmse:0.413164+0.000385 test-rmse:0.423978+0.003621
## [670] train-rmse:0.413155+0.000387 test-rmse:0.423978+0.003620
## [671] train-rmse:0.413142+0.000386 test-rmse:0.423976+0.003618
## [672] train-rmse:0.413131+0.000385 test-rmse:0.423975+0.003616
## [673] train-rmse:0.413121+0.000389 test-rmse:0.423972+0.003614
## [674] train-rmse:0.413109+0.000393 test-rmse:0.423971+0.003615
## [675] train-rmse:0.413099+0.000391 test-rmse:0.423974+0.003617
## [676] train-rmse:0.413089+0.000392 test-rmse:0.423975+0.003618
## [677] train-rmse:0.413076+0.000394 test-rmse:0.423975+0.003618
## [678] train-rmse:0.413064+0.000395 test-rmse:0.423972+0.003619
## [679] train-rmse:0.413052+0.000395 test-rmse:0.423971+0.003619
## [680] train-rmse:0.413041+0.000395 test-rmse:0.423971+0.003619
## [681] train-rmse:0.413028+0.000396 test-rmse:0.423974+0.003618
## [682] train-rmse:0.413016+0.000396 test-rmse:0.423976+0.003623
## [683] train-rmse:0.413003+0.000396 test-rmse:0.423975+0.003621
## [684] train-rmse:0.412986+0.000393 test-rmse:0.423972+0.003621
## [685] train-rmse:0.412972+0.000396 test-rmse:0.423972+0.003620
## [686] train-rmse:0.412959+0.000394 test-rmse:0.423968+0.003619
## [687] train-rmse:0.412947+0.000393 test-rmse:0.423969+0.003619
## [688] train-rmse:0.412936+0.000395 test-rmse:0.423973+0.003621
## [689] train-rmse:0.412924+0.000392 test-rmse:0.423975+0.003622
## [690] train-rmse:0.412912+0.000395 test-rmse:0.423976+0.003624
## [691] train-rmse:0.412903+0.000395 test-rmse:0.423977+0.003627
## [692] train-rmse:0.412890+0.000394 test-rmse:0.423978+0.003628
## [693] train-rmse:0.412878+0.000393 test-rmse:0.423981+0.003629
## [694] train-rmse:0.412867+0.000389 test-rmse:0.423984+0.003627
## [695] train-rmse:0.412857+0.000392 test-rmse:0.423983+0.003626
## [696] train-rmse:0.412841+0.000394 test-rmse:0.423983+0.003624
## [697] train-rmse:0.412832+0.000396 test-rmse:0.423982+0.003625
## [698] train-rmse:0.412820+0.000393 test-rmse:0.423981+0.003625
## [699] train-rmse:0.412809+0.000393 test-rmse:0.423986+0.003627
## [700] train-rmse:0.412794+0.000395 test-rmse:0.423989+0.003624
## [701] train-rmse:0.412782+0.000399 test-rmse:0.423990+0.003626
## [702] train-rmse:0.412771+0.000398 test-rmse:0.423991+0.003627
## [703] train-rmse:0.412758+0.000403 test-rmse:0.423990+0.003626
## [704] train-rmse:0.412746+0.000405 test-rmse:0.423991+0.003626
## [705] train-rmse:0.412735+0.000409 test-rmse:0.423989+0.003624
## [706] train-rmse:0.412724+0.000405 test-rmse:0.423991+0.003625
## [707] train-rmse:0.412712+0.000404 test-rmse:0.423994+0.003626
## [708] train-rmse:0.412699+0.000404 test-rmse:0.423995+0.003627
## [709] train-rmse:0.412685+0.000401 test-rmse:0.423996+0.003626
## [710] train-rmse:0.412674+0.000403 test-rmse:0.424000+0.003628
## [711] train-rmse:0.412663+0.000407 test-rmse:0.423999+0.003630
## [712] train-rmse:0.412654+0.000408 test-rmse:0.423999+0.003631
## [713] train-rmse:0.412640+0.000408 test-rmse:0.423999+0.003631
## [714] train-rmse:0.412629+0.000407 test-rmse:0.423998+0.003632
## [715] train-rmse:0.412618+0.000406 test-rmse:0.424003+0.003632
## [716] train-rmse:0.412609+0.000406 test-rmse:0.424004+0.003633
## [717] train-rmse:0.412600+0.000406 test-rmse:0.424004+0.003633
## [718] train-rmse:0.412590+0.000404 test-rmse:0.424006+0.003634
## [719] train-rmse:0.412578+0.000403 test-rmse:0.424006+0.003635
## [720] train-rmse:0.412562+0.000403 test-rmse:0.424008+0.003636
## [721] train-rmse:0.412552+0.000403 test-rmse:0.424009+0.003638
## [722] train-rmse:0.412538+0.000403 test-rmse:0.424010+0.003636
## [723] train-rmse:0.412527+0.000403 test-rmse:0.424011+0.003639
## [724] train-rmse:0.412514+0.000401 test-rmse:0.424011+0.003639
## [725] train-rmse:0.412500+0.000400 test-rmse:0.424011+0.003636
## [726] train-rmse:0.412489+0.000401 test-rmse:0.424008+0.003633
## [727] train-rmse:0.412478+0.000405 test-rmse:0.424008+0.003634
## [728] train-rmse:0.412466+0.000404 test-rmse:0.424006+0.003636
## [729] train-rmse:0.412452+0.000404 test-rmse:0.424006+0.003642
## [730] train-rmse:0.412437+0.000400 test-rmse:0.424008+0.003645
## [731] train-rmse:0.412423+0.000398 test-rmse:0.424007+0.003644
## [732] train-rmse:0.412417+0.000400 test-rmse:0.424010+0.003646
## [733] train-rmse:0.412404+0.000399 test-rmse:0.424011+0.003649
## [734] train-rmse:0.412390+0.000400 test-rmse:0.424011+0.003648
## [735] train-rmse:0.412378+0.000402 test-rmse:0.424011+0.003651
## [736] train-rmse:0.412364+0.000404 test-rmse:0.424013+0.003650
## [737] train-rmse:0.412352+0.000401 test-rmse:0.424014+0.003650
## [738] train-rmse:0.412340+0.000396 test-rmse:0.424014+0.003652
## [739] train-rmse:0.412332+0.000395 test-rmse:0.424016+0.003652
## [740] train-rmse:0.412317+0.000393 test-rmse:0.424015+0.003652
## [741] train-rmse:0.412304+0.000390 test-rmse:0.424017+0.003654
## [742] train-rmse:0.412290+0.000388 test-rmse:0.424019+0.003656
## [743] train-rmse:0.412278+0.000388 test-rmse:0.424022+0.003655
## [744] train-rmse:0.412270+0.000390 test-rmse:0.424023+0.003654
## [745] train-rmse:0.412259+0.000389 test-rmse:0.424023+0.003655
## [746] train-rmse:0.412242+0.000392 test-rmse:0.424021+0.003653
## [747] train-rmse:0.412230+0.000394 test-rmse:0.424018+0.003654
## [748] train-rmse:0.412220+0.000395 test-rmse:0.424019+0.003654
## [749] train-rmse:0.412207+0.000394 test-rmse:0.424018+0.003655
## [750] train-rmse:0.412195+0.000396 test-rmse:0.424021+0.003656
## [751] train-rmse:0.412184+0.000392 test-rmse:0.424021+0.003656
## [752] train-rmse:0.412171+0.000391 test-rmse:0.424022+0.003655
## [753] train-rmse:0.412160+0.000392 test-rmse:0.424024+0.003658
## [754] train-rmse:0.412147+0.000392 test-rmse:0.424027+0.003657
## [755] train-rmse:0.412136+0.000392 test-rmse:0.424029+0.003659
## [756] train-rmse:0.412125+0.000391 test-rmse:0.424029+0.003658
## [757] train-rmse:0.412112+0.000390 test-rmse:0.424030+0.003659
## [758] train-rmse:0.412099+0.000387 test-rmse:0.424028+0.003660
## [759] train-rmse:0.412086+0.000387 test-rmse:0.424029+0.003660
## [760] train-rmse:0.412077+0.000388 test-rmse:0.424030+0.003658
## [761] train-rmse:0.412063+0.000384 test-rmse:0.424031+0.003657
## [762] train-rmse:0.412051+0.000382 test-rmse:0.424030+0.003656
## [763] train-rmse:0.412041+0.000381 test-rmse:0.424031+0.003656
## [764] train-rmse:0.412029+0.000380 test-rmse:0.424031+0.003660
## [765] train-rmse:0.412014+0.000378 test-rmse:0.424030+0.003659
## [766] train-rmse:0.412000+0.000377 test-rmse:0.424028+0.003656
## [767] train-rmse:0.411988+0.000378 test-rmse:0.424027+0.003657
## [768] train-rmse:0.411975+0.000379 test-rmse:0.424028+0.003659
## [769] train-rmse:0.411965+0.000380 test-rmse:0.424031+0.003657
## [770] train-rmse:0.411952+0.000384 test-rmse:0.424033+0.003656
## [771] train-rmse:0.411938+0.000385 test-rmse:0.424033+0.003656
## [772] train-rmse:0.411928+0.000388 test-rmse:0.424035+0.003657
## [773] train-rmse:0.411915+0.000389 test-rmse:0.424035+0.003658
## [774] train-rmse:0.411903+0.000391 test-rmse:0.424038+0.003658
## [775] train-rmse:0.411894+0.000393 test-rmse:0.424037+0.003656
## [776] train-rmse:0.411884+0.000395 test-rmse:0.424037+0.003655
## [777] train-rmse:0.411875+0.000396 test-rmse:0.424034+0.003653
## [778] train-rmse:0.411865+0.000395 test-rmse:0.424036+0.003655
## [779] train-rmse:0.411855+0.000396 test-rmse:0.424038+0.003655
## [780] train-rmse:0.411845+0.000398 test-rmse:0.424037+0.003653
## [781] train-rmse:0.411833+0.000399 test-rmse:0.424038+0.003654
## [782] train-rmse:0.411827+0.000399 test-rmse:0.424037+0.003654
## [783] train-rmse:0.411818+0.000401 test-rmse:0.424036+0.003653
## [784] train-rmse:0.411809+0.000399 test-rmse:0.424037+0.003653
## [785] train-rmse:0.411799+0.000400 test-rmse:0.424036+0.003654
## [786] train-rmse:0.411788+0.000398 test-rmse:0.424039+0.003652
## [787] train-rmse:0.411778+0.000396 test-rmse:0.424041+0.003650
## [788] train-rmse:0.411768+0.000392 test-rmse:0.424040+0.003655
## [789] train-rmse:0.411759+0.000388 test-rmse:0.424039+0.003655
## [790] train-rmse:0.411744+0.000386 test-rmse:0.424038+0.003655
## [791] train-rmse:0.411729+0.000385 test-rmse:0.424041+0.003651
## [792] train-rmse:0.411720+0.000385 test-rmse:0.424041+0.003651
## [793] train-rmse:0.411711+0.000387 test-rmse:0.424042+0.003652
## [794] train-rmse:0.411698+0.000386 test-rmse:0.424041+0.003652
## [795] train-rmse:0.411686+0.000384 test-rmse:0.424042+0.003652
## [796] train-rmse:0.411672+0.000387 test-rmse:0.424039+0.003654
## [797] train-rmse:0.411661+0.000385 test-rmse:0.424040+0.003654
## [798] train-rmse:0.411649+0.000382 test-rmse:0.424040+0.003654
## [799] train-rmse:0.411639+0.000381 test-rmse:0.424039+0.003656
## [800] train-rmse:0.411625+0.000383 test-rmse:0.424037+0.003655
## [801] train-rmse:0.411611+0.000385 test-rmse:0.424039+0.003655
## [802] train-rmse:0.411599+0.000385 test-rmse:0.424040+0.003656
## [803] train-rmse:0.411588+0.000386 test-rmse:0.424042+0.003654
## [804] train-rmse:0.411580+0.000385 test-rmse:0.424041+0.003653
## [805] train-rmse:0.411567+0.000385 test-rmse:0.424040+0.003655
## [806] train-rmse:0.411558+0.000386 test-rmse:0.424042+0.003658
## [807] train-rmse:0.411545+0.000384 test-rmse:0.424042+0.003657
## [808] train-rmse:0.411531+0.000383 test-rmse:0.424041+0.003656
## [809] train-rmse:0.411518+0.000382 test-rmse:0.424043+0.003657
## [810] train-rmse:0.411508+0.000381 test-rmse:0.424045+0.003657
## [811] train-rmse:0.411494+0.000375 test-rmse:0.424042+0.003657
## [812] train-rmse:0.411482+0.000374 test-rmse:0.424043+0.003658
## [813] train-rmse:0.411471+0.000377 test-rmse:0.424042+0.003658
## [814] train-rmse:0.411456+0.000378 test-rmse:0.424044+0.003660
## [815] train-rmse:0.411442+0.000377 test-rmse:0.424043+0.003662
## [816] train-rmse:0.411431+0.000378 test-rmse:0.424044+0.003662
## [817] train-rmse:0.411422+0.000379 test-rmse:0.424048+0.003660
## [818] train-rmse:0.411414+0.000382 test-rmse:0.424049+0.003662
## [819] train-rmse:0.411401+0.000385 test-rmse:0.424049+0.003658
## [820] train-rmse:0.411391+0.000382 test-rmse:0.424046+0.003658
## [821] train-rmse:0.411380+0.000384 test-rmse:0.424050+0.003656
## [822] train-rmse:0.411367+0.000382 test-rmse:0.424047+0.003655
## [823] train-rmse:0.411354+0.000384 test-rmse:0.424046+0.003656
## [824] train-rmse:0.411344+0.000384 test-rmse:0.424048+0.003656
## [825] train-rmse:0.411329+0.000385 test-rmse:0.424048+0.003652
## [826] train-rmse:0.411315+0.000388 test-rmse:0.424049+0.003652
## [827] train-rmse:0.411304+0.000387 test-rmse:0.424051+0.003654
## [828] train-rmse:0.411295+0.000385 test-rmse:0.424053+0.003653
## [829] train-rmse:0.411285+0.000383 test-rmse:0.424053+0.003651
## [830] train-rmse:0.411274+0.000387 test-rmse:0.424053+0.003649
## [831] train-rmse:0.411261+0.000387 test-rmse:0.424054+0.003649
## [832] train-rmse:0.411248+0.000388 test-rmse:0.424054+0.003648
## [833] train-rmse:0.411237+0.000389 test-rmse:0.424058+0.003647
## [834] train-rmse:0.411226+0.000390 test-rmse:0.424060+0.003650
## [835] train-rmse:0.411215+0.000390 test-rmse:0.424060+0.003649
## [836] train-rmse:0.411201+0.000388 test-rmse:0.424060+0.003647
## [837] train-rmse:0.411191+0.000392 test-rmse:0.424061+0.003648
## [838] train-rmse:0.411178+0.000393 test-rmse:0.424059+0.003647
## [839] train-rmse:0.411163+0.000396 test-rmse:0.424056+0.003646
## [840] train-rmse:0.411151+0.000397 test-rmse:0.424057+0.003647
## [841] train-rmse:0.411141+0.000394 test-rmse:0.424058+0.003646
## [842] train-rmse:0.411130+0.000395 test-rmse:0.424059+0.003646
## [843] train-rmse:0.411118+0.000397 test-rmse:0.424058+0.003645
## [844] train-rmse:0.411108+0.000398 test-rmse:0.424060+0.003644
## [845] train-rmse:0.411094+0.000399 test-rmse:0.424058+0.003641
## [846] train-rmse:0.411081+0.000402 test-rmse:0.424058+0.003644
## [847] train-rmse:0.411069+0.000404 test-rmse:0.424056+0.003643
## [848] train-rmse:0.411056+0.000408 test-rmse:0.424056+0.003641
## [849] train-rmse:0.411044+0.000404 test-rmse:0.424057+0.003641
## [850] train-rmse:0.411033+0.000409 test-rmse:0.424058+0.003636
## [851] train-rmse:0.411021+0.000413 test-rmse:0.424057+0.003636
## [852] train-rmse:0.411010+0.000412 test-rmse:0.424057+0.003638
## [853] train-rmse:0.410999+0.000411 test-rmse:0.424059+0.003638
## [854] train-rmse:0.410989+0.000412 test-rmse:0.424060+0.003639
## [855] train-rmse:0.410978+0.000411 test-rmse:0.424061+0.003638
## [856] train-rmse:0.410966+0.000411 test-rmse:0.424062+0.003640
## [857] train-rmse:0.410951+0.000408 test-rmse:0.424062+0.003642
## [858] train-rmse:0.410941+0.000410 test-rmse:0.424063+0.003641
## [859] train-rmse:0.410933+0.000410 test-rmse:0.424065+0.003638
## [860] train-rmse:0.410925+0.000413 test-rmse:0.424067+0.003638
## [861] train-rmse:0.410912+0.000413 test-rmse:0.424068+0.003638
## [862] train-rmse:0.410903+0.000416 test-rmse:0.424069+0.003635
## [863] train-rmse:0.410891+0.000418 test-rmse:0.424068+0.003635
## [864] train-rmse:0.410882+0.000420 test-rmse:0.424068+0.003632
## [865] train-rmse:0.410870+0.000421 test-rmse:0.424069+0.003633
## [866] train-rmse:0.410861+0.000422 test-rmse:0.424067+0.003635
## [867] train-rmse:0.410850+0.000421 test-rmse:0.424069+0.003636
## [868] train-rmse:0.410839+0.000421 test-rmse:0.424071+0.003636
## [869] train-rmse:0.410826+0.000422 test-rmse:0.424073+0.003637
## [870] train-rmse:0.410817+0.000420 test-rmse:0.424075+0.003639
## [871] train-rmse:0.410805+0.000420 test-rmse:0.424076+0.003641
## [872] train-rmse:0.410793+0.000421 test-rmse:0.424078+0.003644
## [873] train-rmse:0.410781+0.000422 test-rmse:0.424081+0.003647
## [874] train-rmse:0.410772+0.000424 test-rmse:0.424082+0.003650
## [875] train-rmse:0.410760+0.000426 test-rmse:0.424084+0.003647
## [876] train-rmse:0.410749+0.000427 test-rmse:0.424084+0.003648
## [877] train-rmse:0.410740+0.000430 test-rmse:0.424084+0.003651
## [878] train-rmse:0.410726+0.000429 test-rmse:0.424088+0.003649
## [879] train-rmse:0.410717+0.000432 test-rmse:0.424088+0.003650
## [880] train-rmse:0.410710+0.000433 test-rmse:0.424088+0.003647
## [881] train-rmse:0.410699+0.000434 test-rmse:0.424091+0.003647
## [882] train-rmse:0.410690+0.000434 test-rmse:0.424094+0.003647
## [883] train-rmse:0.410676+0.000436 test-rmse:0.424096+0.003646
## [884] train-rmse:0.410666+0.000437 test-rmse:0.424095+0.003647
## [885] train-rmse:0.410656+0.000438 test-rmse:0.424095+0.003647
## [886] train-rmse:0.410649+0.000443 test-rmse:0.424098+0.003647
## [887] train-rmse:0.410638+0.000445 test-rmse:0.424099+0.003648
## [888] train-rmse:0.410625+0.000446 test-rmse:0.424105+0.003650
## [889] train-rmse:0.410612+0.000449 test-rmse:0.424107+0.003651
## [890] train-rmse:0.410602+0.000451 test-rmse:0.424107+0.003652
## [891] train-rmse:0.410593+0.000454 test-rmse:0.424109+0.003653
## [892] train-rmse:0.410584+0.000455 test-rmse:0.424110+0.003655
## [893] train-rmse:0.410573+0.000455 test-rmse:0.424110+0.003653
## [894] train-rmse:0.410559+0.000454 test-rmse:0.424113+0.003651
## [895] train-rmse:0.410549+0.000453 test-rmse:0.424114+0.003653
## [896] train-rmse:0.410537+0.000451 test-rmse:0.424110+0.003653
## [897] train-rmse:0.410528+0.000452 test-rmse:0.424114+0.003657
## [898] train-rmse:0.410517+0.000451 test-rmse:0.424116+0.003655
## [899] train-rmse:0.410507+0.000454 test-rmse:0.424116+0.003657
## [900] train-rmse:0.410498+0.000453 test-rmse:0.424115+0.003659
## [901] train-rmse:0.410487+0.000453 test-rmse:0.424114+0.003658
## [902] train-rmse:0.410475+0.000451 test-rmse:0.424114+0.003659
## [903] train-rmse:0.410462+0.000451 test-rmse:0.424113+0.003663
## [904] train-rmse:0.410452+0.000449 test-rmse:0.424112+0.003666
## [905] train-rmse:0.410441+0.000452 test-rmse:0.424115+0.003667
## [906] train-rmse:0.410432+0.000451 test-rmse:0.424115+0.003668
## [907] train-rmse:0.410420+0.000448 test-rmse:0.424115+0.003671
## [908] train-rmse:0.410408+0.000446 test-rmse:0.424115+0.003671
## [909] train-rmse:0.410395+0.000446 test-rmse:0.424121+0.003672
## [910] train-rmse:0.410382+0.000448 test-rmse:0.424120+0.003672
## [911] train-rmse:0.410371+0.000447 test-rmse:0.424123+0.003668
## [912] train-rmse:0.410362+0.000450 test-rmse:0.424124+0.003669
## [913] train-rmse:0.410348+0.000449 test-rmse:0.424123+0.003671
## [914] train-rmse:0.410335+0.000450 test-rmse:0.424125+0.003669
## [915] train-rmse:0.410325+0.000451 test-rmse:0.424127+0.003669
## [916] train-rmse:0.410310+0.000451 test-rmse:0.424126+0.003671
## [917] train-rmse:0.410297+0.000455 test-rmse:0.424129+0.003672
## [918] train-rmse:0.410285+0.000454 test-rmse:0.424130+0.003672
## [919] train-rmse:0.410270+0.000456 test-rmse:0.424130+0.003674
## [920] train-rmse:0.410258+0.000454 test-rmse:0.424131+0.003674
## [921] train-rmse:0.410246+0.000455 test-rmse:0.424134+0.003669
## [922] train-rmse:0.410235+0.000457 test-rmse:0.424135+0.003670
## [923] train-rmse:0.410226+0.000459 test-rmse:0.424134+0.003669
## [924] train-rmse:0.410210+0.000459 test-rmse:0.424131+0.003662
## [925] train-rmse:0.410197+0.000459 test-rmse:0.424136+0.003663
## [926] train-rmse:0.410185+0.000459 test-rmse:0.424136+0.003662
## [927] train-rmse:0.410168+0.000460 test-rmse:0.424136+0.003660
## [928] train-rmse:0.410156+0.000461 test-rmse:0.424139+0.003661
## [929] train-rmse:0.410143+0.000459 test-rmse:0.424137+0.003662
## [930] train-rmse:0.410132+0.000459 test-rmse:0.424135+0.003663
## [931] train-rmse:0.410121+0.000462 test-rmse:0.424134+0.003664
## [932] train-rmse:0.410108+0.000465 test-rmse:0.424135+0.003664
## [933] train-rmse:0.410096+0.000464 test-rmse:0.424138+0.003663
## [934] train-rmse:0.410083+0.000468 test-rmse:0.424140+0.003662
## [935] train-rmse:0.410075+0.000470 test-rmse:0.424140+0.003663
## [936] train-rmse:0.410063+0.000469 test-rmse:0.424141+0.003664
## [937] train-rmse:0.410051+0.000469 test-rmse:0.424142+0.003666
## [938] train-rmse:0.410039+0.000471 test-rmse:0.424144+0.003667
## [939] train-rmse:0.410028+0.000472 test-rmse:0.424148+0.003667
## [940] train-rmse:0.410020+0.000473 test-rmse:0.424149+0.003668
## [941] train-rmse:0.410006+0.000472 test-rmse:0.424150+0.003668
## [942] train-rmse:0.409995+0.000472 test-rmse:0.424150+0.003669
## [943] train-rmse:0.409984+0.000472 test-rmse:0.424151+0.003671
## [944] train-rmse:0.409972+0.000471 test-rmse:0.424152+0.003672
## [945] train-rmse:0.409961+0.000469 test-rmse:0.424156+0.003669
## [946] train-rmse:0.409950+0.000470 test-rmse:0.424154+0.003669
## [947] train-rmse:0.409936+0.000470 test-rmse:0.424155+0.003668
## [948] train-rmse:0.409925+0.000470 test-rmse:0.424154+0.003669
## [949] train-rmse:0.409914+0.000472 test-rmse:0.424152+0.003668
## [950] train-rmse:0.409905+0.000469 test-rmse:0.424154+0.003668
## [951] train-rmse:0.409894+0.000472 test-rmse:0.424154+0.003666
## [952] train-rmse:0.409882+0.000474 test-rmse:0.424154+0.003665
## [953] train-rmse:0.409872+0.000472 test-rmse:0.424152+0.003666
## [954] train-rmse:0.409860+0.000469 test-rmse:0.424150+0.003668
## [955] train-rmse:0.409845+0.000465 test-rmse:0.424148+0.003669
## [956] train-rmse:0.409835+0.000462 test-rmse:0.424149+0.003670
## [957] train-rmse:0.409822+0.000462 test-rmse:0.424147+0.003673
## [958] train-rmse:0.409814+0.000459 test-rmse:0.424144+0.003673
## [959] train-rmse:0.409803+0.000461 test-rmse:0.424145+0.003673
## [960] train-rmse:0.409792+0.000459 test-rmse:0.424145+0.003671
## [961] train-rmse:0.409780+0.000460 test-rmse:0.424144+0.003671
## [962] train-rmse:0.409771+0.000454 test-rmse:0.424144+0.003673
## [963] train-rmse:0.409762+0.000455 test-rmse:0.424144+0.003674
## [964] train-rmse:0.409750+0.000452 test-rmse:0.424144+0.003676
## [965] train-rmse:0.409737+0.000449 test-rmse:0.424145+0.003676
## [966] train-rmse:0.409727+0.000448 test-rmse:0.424146+0.003675
## [967] train-rmse:0.409719+0.000446 test-rmse:0.424149+0.003675
## [968] train-rmse:0.409707+0.000447 test-rmse:0.424149+0.003673
## [969] train-rmse:0.409695+0.000448 test-rmse:0.424151+0.003676
## [970] train-rmse:0.409684+0.000451 test-rmse:0.424151+0.003679
## [971] train-rmse:0.409674+0.000448 test-rmse:0.424153+0.003677
## [972] train-rmse:0.409662+0.000445 test-rmse:0.424153+0.003678
## [973] train-rmse:0.409653+0.000443 test-rmse:0.424153+0.003679
## [974] train-rmse:0.409641+0.000445 test-rmse:0.424153+0.003674
## [975] train-rmse:0.409630+0.000448 test-rmse:0.424154+0.003674
## [976] train-rmse:0.409619+0.000446 test-rmse:0.424154+0.003673
## [977] train-rmse:0.409607+0.000447 test-rmse:0.424151+0.003670
## [978] train-rmse:0.409595+0.000447 test-rmse:0.424151+0.003671
## [979] train-rmse:0.409581+0.000448 test-rmse:0.424151+0.003671
## [980] train-rmse:0.409568+0.000450 test-rmse:0.424152+0.003670
## [981] train-rmse:0.409556+0.000453 test-rmse:0.424152+0.003669
## [982] train-rmse:0.409546+0.000455 test-rmse:0.424152+0.003669
## [983] train-rmse:0.409536+0.000459 test-rmse:0.424153+0.003668
## [984] train-rmse:0.409525+0.000460 test-rmse:0.424154+0.003667
## [985] train-rmse:0.409512+0.000464 test-rmse:0.424153+0.003665
## [986] train-rmse:0.409499+0.000463 test-rmse:0.424156+0.003665
## [987] train-rmse:0.409486+0.000466 test-rmse:0.424158+0.003664
## [988] train-rmse:0.409473+0.000467 test-rmse:0.424157+0.003665
## [989] train-rmse:0.409463+0.000466 test-rmse:0.424155+0.003669
## [990] train-rmse:0.409453+0.000466 test-rmse:0.424158+0.003669
## [991] train-rmse:0.409438+0.000468 test-rmse:0.424159+0.003667
## [992] train-rmse:0.409427+0.000467 test-rmse:0.424162+0.003669
## [993] train-rmse:0.409414+0.000467 test-rmse:0.424159+0.003668
## [994] train-rmse:0.409401+0.000469 test-rmse:0.424158+0.003667
## [995] train-rmse:0.409392+0.000471 test-rmse:0.424155+0.003668
## [996] train-rmse:0.409382+0.000470 test-rmse:0.424155+0.003668
## [997] train-rmse:0.409371+0.000470 test-rmse:0.424158+0.003668
## [998] train-rmse:0.409362+0.000471 test-rmse:0.424158+0.003667
## [999] train-rmse:0.409350+0.000473 test-rmse:0.424159+0.003666
## [1000] train-rmse:0.409339+0.000472 test-rmse:0.424161+0.003664
## [1001] train-rmse:0.409331+0.000476 test-rmse:0.424162+0.003664
## [1002] train-rmse:0.409323+0.000474 test-rmse:0.424161+0.003665
## [1003] train-rmse:0.409314+0.000474 test-rmse:0.424159+0.003662
## [1004] train-rmse:0.409303+0.000473 test-rmse:0.424159+0.003662
## [1005] train-rmse:0.409292+0.000475 test-rmse:0.424158+0.003662
## [1006] train-rmse:0.409282+0.000475 test-rmse:0.424159+0.003660
## [1007] train-rmse:0.409270+0.000477 test-rmse:0.424161+0.003661
## [1008] train-rmse:0.409259+0.000474 test-rmse:0.424162+0.003660
## [1009] train-rmse:0.409249+0.000474 test-rmse:0.424162+0.003662
## [1010] train-rmse:0.409239+0.000475 test-rmse:0.424164+0.003663
## [1011] train-rmse:0.409230+0.000474 test-rmse:0.424164+0.003664
## [1012] train-rmse:0.409217+0.000475 test-rmse:0.424163+0.003666
## [1013] train-rmse:0.409206+0.000476 test-rmse:0.424164+0.003666
## [1014] train-rmse:0.409197+0.000474 test-rmse:0.424163+0.003668
## [1015] train-rmse:0.409186+0.000478 test-rmse:0.424161+0.003664
## [1016] train-rmse:0.409177+0.000482 test-rmse:0.424162+0.003666
## [1017] train-rmse:0.409163+0.000484 test-rmse:0.424165+0.003669
## [1018] train-rmse:0.409151+0.000484 test-rmse:0.424167+0.003670
## [1019] train-rmse:0.409139+0.000483 test-rmse:0.424167+0.003664
## [1020] train-rmse:0.409126+0.000483 test-rmse:0.424167+0.003662
## [1021] train-rmse:0.409117+0.000484 test-rmse:0.424169+0.003660
## [1022] train-rmse:0.409106+0.000488 test-rmse:0.424169+0.003659
## [1023] train-rmse:0.409096+0.000488 test-rmse:0.424167+0.003659
## [1024] train-rmse:0.409086+0.000488 test-rmse:0.424166+0.003657
## [1025] train-rmse:0.409075+0.000489 test-rmse:0.424166+0.003657
## [1026] train-rmse:0.409062+0.000488 test-rmse:0.424167+0.003659
## [1027] train-rmse:0.409052+0.000491 test-rmse:0.424168+0.003657
## [1028] train-rmse:0.409041+0.000491 test-rmse:0.424167+0.003659
## [1029] train-rmse:0.409028+0.000494 test-rmse:0.424169+0.003656
## [1030] train-rmse:0.409016+0.000496 test-rmse:0.424170+0.003657
## [1031] train-rmse:0.409007+0.000496 test-rmse:0.424172+0.003657
## [1032] train-rmse:0.408994+0.000494 test-rmse:0.424172+0.003655
## [1033] train-rmse:0.408984+0.000492 test-rmse:0.424171+0.003657
## [1034] train-rmse:0.408977+0.000491 test-rmse:0.424172+0.003657
## [1035] train-rmse:0.408968+0.000493 test-rmse:0.424173+0.003657
## [1036] train-rmse:0.408955+0.000491 test-rmse:0.424174+0.003657
## [1037] train-rmse:0.408944+0.000489 test-rmse:0.424177+0.003654
## [1038] train-rmse:0.408932+0.000489 test-rmse:0.424174+0.003657
## [1039] train-rmse:0.408920+0.000487 test-rmse:0.424177+0.003658
## [1040] train-rmse:0.408912+0.000488 test-rmse:0.424178+0.003658
## [1041] train-rmse:0.408899+0.000490 test-rmse:0.424177+0.003661
## [1042] train-rmse:0.408888+0.000488 test-rmse:0.424176+0.003662
## [1043] train-rmse:0.408874+0.000486 test-rmse:0.424176+0.003664
## [1044] train-rmse:0.408863+0.000486 test-rmse:0.424176+0.003665
## [1045] train-rmse:0.408852+0.000487 test-rmse:0.424177+0.003668
## [1046] train-rmse:0.408843+0.000488 test-rmse:0.424179+0.003669
## [1047] train-rmse:0.408832+0.000486 test-rmse:0.424178+0.003668
## [1048] train-rmse:0.408823+0.000488 test-rmse:0.424183+0.003668
## [1049] train-rmse:0.408814+0.000491 test-rmse:0.424184+0.003666
## [1050] train-rmse:0.408807+0.000489 test-rmse:0.424184+0.003666
## [1051] train-rmse:0.408799+0.000490 test-rmse:0.424184+0.003666
## [1052] train-rmse:0.408789+0.000486 test-rmse:0.424187+0.003664
## [1053] train-rmse:0.408778+0.000487 test-rmse:0.424188+0.003666
## [1054] train-rmse:0.408770+0.000483 test-rmse:0.424189+0.003668
## [1055] train-rmse:0.408759+0.000483 test-rmse:0.424189+0.003668
## [1056] train-rmse:0.408750+0.000482 test-rmse:0.424191+0.003668
## [1057] train-rmse:0.408739+0.000483 test-rmse:0.424191+0.003668
## [1058] train-rmse:0.408728+0.000482 test-rmse:0.424192+0.003669
## [1059] train-rmse:0.408718+0.000481 test-rmse:0.424193+0.003667
## [1060] train-rmse:0.408709+0.000479 test-rmse:0.424195+0.003668
## [1061] train-rmse:0.408700+0.000481 test-rmse:0.424194+0.003666
## [1062] train-rmse:0.408691+0.000481 test-rmse:0.424193+0.003666
## [1063] train-rmse:0.408682+0.000484 test-rmse:0.424193+0.003666
## [1064] train-rmse:0.408672+0.000484 test-rmse:0.424193+0.003665
## [1065] train-rmse:0.408663+0.000483 test-rmse:0.424192+0.003666
## [1066] train-rmse:0.408653+0.000482 test-rmse:0.424194+0.003665
## [1067] train-rmse:0.408641+0.000482 test-rmse:0.424198+0.003665
## [1068] train-rmse:0.408631+0.000481 test-rmse:0.424197+0.003667
## [1069] train-rmse:0.408619+0.000481 test-rmse:0.424198+0.003668
## [1070] train-rmse:0.408608+0.000478 test-rmse:0.424199+0.003669
## [1071] train-rmse:0.408600+0.000478 test-rmse:0.424199+0.003668
## [1072] train-rmse:0.408590+0.000478 test-rmse:0.424203+0.003668
## [1073] train-rmse:0.408578+0.000478 test-rmse:0.424203+0.003669
## [1074] train-rmse:0.408568+0.000477 test-rmse:0.424203+0.003667
## [1075] train-rmse:0.408555+0.000475 test-rmse:0.424203+0.003667
## [1076] train-rmse:0.408545+0.000476 test-rmse:0.424204+0.003668
## [1077] train-rmse:0.408536+0.000474 test-rmse:0.424203+0.003669
## [1078] train-rmse:0.408527+0.000473 test-rmse:0.424204+0.003669
## [1079] train-rmse:0.408517+0.000470 test-rmse:0.424203+0.003669
## [1080] train-rmse:0.408505+0.000471 test-rmse:0.424203+0.003668
## [1081] train-rmse:0.408493+0.000471 test-rmse:0.424201+0.003667
## [1082] train-rmse:0.408480+0.000470 test-rmse:0.424203+0.003665
## [1083] train-rmse:0.408470+0.000469 test-rmse:0.424204+0.003665
## [1084] train-rmse:0.408458+0.000470 test-rmse:0.424201+0.003666
## [1085] train-rmse:0.408448+0.000468 test-rmse:0.424202+0.003668
## [1086] train-rmse:0.408438+0.000468 test-rmse:0.424202+0.003665
## [1087] train-rmse:0.408423+0.000470 test-rmse:0.424202+0.003666
## [1088] train-rmse:0.408413+0.000466 test-rmse:0.424204+0.003668
## [1089] train-rmse:0.408400+0.000465 test-rmse:0.424205+0.003667
## [1090] train-rmse:0.408388+0.000466 test-rmse:0.424205+0.003669
## [1091] train-rmse:0.408374+0.000465 test-rmse:0.424205+0.003670
## [1092] train-rmse:0.408367+0.000465 test-rmse:0.424208+0.003671
## [1093] train-rmse:0.408357+0.000463 test-rmse:0.424209+0.003673
## [1094] train-rmse:0.408350+0.000466 test-rmse:0.424209+0.003673
## [1095] train-rmse:0.408339+0.000468 test-rmse:0.424209+0.003672
## [1096] train-rmse:0.408329+0.000467 test-rmse:0.424210+0.003671
## [1097] train-rmse:0.408317+0.000469 test-rmse:0.424211+0.003669
## [1098] train-rmse:0.408307+0.000472 test-rmse:0.424212+0.003669
## [1099] train-rmse:0.408296+0.000473 test-rmse:0.424215+0.003669
## [1100] train-rmse:0.408285+0.000475 test-rmse:0.424214+0.003663
## [1101] train-rmse:0.408276+0.000477 test-rmse:0.424215+0.003663
## [1102] train-rmse:0.408268+0.000481 test-rmse:0.424217+0.003665
## [1103] train-rmse:0.408261+0.000481 test-rmse:0.424218+0.003666
## [1104] train-rmse:0.408252+0.000483 test-rmse:0.424217+0.003665
## [1105] train-rmse:0.408244+0.000481 test-rmse:0.424216+0.003666
## [1106] train-rmse:0.408235+0.000485 test-rmse:0.424217+0.003664
## [1107] train-rmse:0.408226+0.000484 test-rmse:0.424218+0.003662
## [1108] train-rmse:0.408217+0.000488 test-rmse:0.424219+0.003662
## [1109] train-rmse:0.408203+0.000489 test-rmse:0.424219+0.003661
## [1110] train-rmse:0.408193+0.000490 test-rmse:0.424218+0.003660
## [1111] train-rmse:0.408184+0.000494 test-rmse:0.424219+0.003662
## [1112] train-rmse:0.408172+0.000494 test-rmse:0.424219+0.003661
## [1113] train-rmse:0.408163+0.000494 test-rmse:0.424222+0.003662
## [1114] train-rmse:0.408148+0.000496 test-rmse:0.424220+0.003662
## [1115] train-rmse:0.408141+0.000497 test-rmse:0.424221+0.003658
## [1116] train-rmse:0.408132+0.000500 test-rmse:0.424222+0.003659
## [1117] train-rmse:0.408123+0.000502 test-rmse:0.424224+0.003660
## [1118] train-rmse:0.408116+0.000504 test-rmse:0.424227+0.003661
## [1119] train-rmse:0.408109+0.000506 test-rmse:0.424227+0.003661
## [1120] train-rmse:0.408102+0.000508 test-rmse:0.424228+0.003661
## [1121] train-rmse:0.408090+0.000506 test-rmse:0.424230+0.003658
## [1122] train-rmse:0.408082+0.000506 test-rmse:0.424232+0.003659
## [1123] train-rmse:0.408071+0.000506 test-rmse:0.424230+0.003660
## [1124] train-rmse:0.408062+0.000508 test-rmse:0.424231+0.003657
## [1125] train-rmse:0.408050+0.000509 test-rmse:0.424230+0.003657
## [1126] train-rmse:0.408041+0.000510 test-rmse:0.424231+0.003657
## [1127] train-rmse:0.408031+0.000510 test-rmse:0.424230+0.003658
## [1128] train-rmse:0.408022+0.000512 test-rmse:0.424231+0.003658
## [1129] train-rmse:0.408007+0.000509 test-rmse:0.424230+0.003657
## [1130] train-rmse:0.407996+0.000509 test-rmse:0.424230+0.003657
## [1131] train-rmse:0.407987+0.000507 test-rmse:0.424230+0.003658
## [1132] train-rmse:0.407977+0.000507 test-rmse:0.424234+0.003658
## [1133] train-rmse:0.407965+0.000504 test-rmse:0.424233+0.003659
## [1134] train-rmse:0.407955+0.000502 test-rmse:0.424233+0.003660
## [1135] train-rmse:0.407949+0.000502 test-rmse:0.424234+0.003659
## [1136] train-rmse:0.407941+0.000499 test-rmse:0.424233+0.003658
## [1137] train-rmse:0.407932+0.000500 test-rmse:0.424234+0.003660
## [1138] train-rmse:0.407924+0.000504 test-rmse:0.424234+0.003660
## [1139] train-rmse:0.407914+0.000504 test-rmse:0.424237+0.003661
## [1140] train-rmse:0.407905+0.000504 test-rmse:0.424238+0.003662
## [1141] train-rmse:0.407896+0.000505 test-rmse:0.424240+0.003661
## [1142] train-rmse:0.407887+0.000504 test-rmse:0.424239+0.003663
## [1143] train-rmse:0.407878+0.000505 test-rmse:0.424238+0.003661
## [1144] train-rmse:0.407870+0.000505 test-rmse:0.424238+0.003662
## [1145] train-rmse:0.407864+0.000504 test-rmse:0.424237+0.003663
## [1146] train-rmse:0.407853+0.000504 test-rmse:0.424241+0.003660
## [1147] train-rmse:0.407842+0.000504 test-rmse:0.424241+0.003661
## [1148] train-rmse:0.407832+0.000505 test-rmse:0.424239+0.003662
## [1149] train-rmse:0.407821+0.000502 test-rmse:0.424240+0.003663
## [1150] train-rmse:0.407810+0.000504 test-rmse:0.424242+0.003662
## [1151] train-rmse:0.407805+0.000506 test-rmse:0.424240+0.003661
## [1152] train-rmse:0.407793+0.000504 test-rmse:0.424236+0.003663
## [1153] train-rmse:0.407783+0.000504 test-rmse:0.424239+0.003662
## [1154] train-rmse:0.407775+0.000505 test-rmse:0.424239+0.003661
## [1155] train-rmse:0.407758+0.000506 test-rmse:0.424241+0.003663
## [1156] train-rmse:0.407746+0.000509 test-rmse:0.424240+0.003663
## [1157] train-rmse:0.407733+0.000508 test-rmse:0.424243+0.003664
## [1158] train-rmse:0.407724+0.000510 test-rmse:0.424245+0.003666
## [1159] train-rmse:0.407717+0.000510 test-rmse:0.424246+0.003667
## [1160] train-rmse:0.407706+0.000510 test-rmse:0.424246+0.003665
## [1161] train-rmse:0.407694+0.000512 test-rmse:0.424244+0.003662
## [1162] train-rmse:0.407684+0.000512 test-rmse:0.424245+0.003661
## [1163] train-rmse:0.407673+0.000514 test-rmse:0.424245+0.003661
## [1164] train-rmse:0.407662+0.000514 test-rmse:0.424243+0.003661
## [1165] train-rmse:0.407652+0.000515 test-rmse:0.424244+0.003661
## [1166] train-rmse:0.407639+0.000516 test-rmse:0.424244+0.003662
## [1167] train-rmse:0.407626+0.000518 test-rmse:0.424243+0.003663
## [1168] train-rmse:0.407611+0.000521 test-rmse:0.424243+0.003661
## [1169] train-rmse:0.407600+0.000522 test-rmse:0.424242+0.003662
## [1170] train-rmse:0.407590+0.000519 test-rmse:0.424241+0.003663
## [1171] train-rmse:0.407579+0.000520 test-rmse:0.424243+0.003662
## [1172] train-rmse:0.407564+0.000521 test-rmse:0.424241+0.003659
## [1173] train-rmse:0.407554+0.000524 test-rmse:0.424242+0.003657
## [1174] train-rmse:0.407542+0.000525 test-rmse:0.424238+0.003660
## [1175] train-rmse:0.407533+0.000523 test-rmse:0.424236+0.003662
## [1176] train-rmse:0.407521+0.000521 test-rmse:0.424233+0.003663
## [1177] train-rmse:0.407513+0.000519 test-rmse:0.424235+0.003664
## [1178] train-rmse:0.407500+0.000518 test-rmse:0.424237+0.003663
## [1179] train-rmse:0.407490+0.000516 test-rmse:0.424240+0.003664
## [1180] train-rmse:0.407478+0.000518 test-rmse:0.424240+0.003664
## [1181] train-rmse:0.407466+0.000517 test-rmse:0.424242+0.003667
## [1182] train-rmse:0.407457+0.000518 test-rmse:0.424241+0.003666
## [1183] train-rmse:0.407445+0.000515 test-rmse:0.424239+0.003667
## [1184] train-rmse:0.407433+0.000515 test-rmse:0.424238+0.003667
## [1185] train-rmse:0.407425+0.000519 test-rmse:0.424236+0.003669
## [1186] train-rmse:0.407416+0.000521 test-rmse:0.424235+0.003669
## [1187] train-rmse:0.407403+0.000522 test-rmse:0.424236+0.003667
## [1188] train-rmse:0.407389+0.000520 test-rmse:0.424238+0.003667
## [1189] train-rmse:0.407378+0.000524 test-rmse:0.424240+0.003669
## [1190] train-rmse:0.407365+0.000526 test-rmse:0.424240+0.003670
## [1191] train-rmse:0.407355+0.000529 test-rmse:0.424242+0.003669
## [1192] train-rmse:0.407344+0.000531 test-rmse:0.424242+0.003667
## [1193] train-rmse:0.407333+0.000533 test-rmse:0.424240+0.003669
## [1194] train-rmse:0.407322+0.000533 test-rmse:0.424243+0.003665
## [1195] train-rmse:0.407312+0.000536 test-rmse:0.424243+0.003665
## [1196] train-rmse:0.407301+0.000540 test-rmse:0.424245+0.003665
## [1197] train-rmse:0.407290+0.000540 test-rmse:0.424244+0.003667
## [1198] train-rmse:0.407278+0.000541 test-rmse:0.424241+0.003669
## [1199] train-rmse:0.407263+0.000539 test-rmse:0.424247+0.003667
## [1200] train-rmse:0.407251+0.000539 test-rmse:0.424245+0.003665
## [1201] train-rmse:0.407240+0.000537 test-rmse:0.424243+0.003664
## [1202] train-rmse:0.407229+0.000539 test-rmse:0.424245+0.003667
## [1203] train-rmse:0.407220+0.000543 test-rmse:0.424245+0.003667
## [1204] train-rmse:0.407209+0.000540 test-rmse:0.424246+0.003667
## [1205] train-rmse:0.407198+0.000544 test-rmse:0.424243+0.003665
## [1206] train-rmse:0.407186+0.000543 test-rmse:0.424243+0.003663
## [1207] train-rmse:0.407176+0.000544 test-rmse:0.424245+0.003665
## [1208] train-rmse:0.407166+0.000543 test-rmse:0.424245+0.003665
## [1209] train-rmse:0.407156+0.000547 test-rmse:0.424247+0.003666
## [1210] train-rmse:0.407145+0.000547 test-rmse:0.424246+0.003665
## [1211] train-rmse:0.407137+0.000551 test-rmse:0.424245+0.003663
## [1212] train-rmse:0.407128+0.000553 test-rmse:0.424244+0.003664
## [1213] train-rmse:0.407115+0.000553 test-rmse:0.424242+0.003667
## [1214] train-rmse:0.407106+0.000552 test-rmse:0.424244+0.003668
## [1215] train-rmse:0.407094+0.000553 test-rmse:0.424244+0.003672
## [1216] train-rmse:0.407082+0.000554 test-rmse:0.424245+0.003672
## [1217] train-rmse:0.407072+0.000558 test-rmse:0.424243+0.003670
## [1218] train-rmse:0.407061+0.000560 test-rmse:0.424242+0.003672
## [1219] train-rmse:0.407051+0.000564 test-rmse:0.424241+0.003670
## [1220] train-rmse:0.407041+0.000564 test-rmse:0.424242+0.003669
## [1221] train-rmse:0.407031+0.000565 test-rmse:0.424243+0.003669
## [1222] train-rmse:0.407021+0.000565 test-rmse:0.424243+0.003669
## [1223] train-rmse:0.407009+0.000568 test-rmse:0.424242+0.003669
## [1224] train-rmse:0.407000+0.000569 test-rmse:0.424242+0.003669
## [1225] train-rmse:0.406990+0.000570 test-rmse:0.424242+0.003671
## [1226] train-rmse:0.406980+0.000570 test-rmse:0.424241+0.003668
## [1227] train-rmse:0.406970+0.000569 test-rmse:0.424242+0.003669
## [1228] train-rmse:0.406957+0.000570 test-rmse:0.424246+0.003672
## [1229] train-rmse:0.406948+0.000567 test-rmse:0.424248+0.003674
## [1230] train-rmse:0.406939+0.000565 test-rmse:0.424247+0.003676
## [1231] train-rmse:0.406928+0.000562 test-rmse:0.424245+0.003676
## [1232] train-rmse:0.406920+0.000560 test-rmse:0.424246+0.003676
## [1233] train-rmse:0.406906+0.000563 test-rmse:0.424249+0.003673
## [1234] train-rmse:0.406897+0.000563 test-rmse:0.424252+0.003679
## [1235] train-rmse:0.406888+0.000565 test-rmse:0.424254+0.003680
## [1236] train-rmse:0.406878+0.000565 test-rmse:0.424255+0.003679
## [1237] train-rmse:0.406867+0.000567 test-rmse:0.424254+0.003680
## [1238] train-rmse:0.406856+0.000567 test-rmse:0.424256+0.003681
## [1239] train-rmse:0.406842+0.000564 test-rmse:0.424255+0.003679
## [1240] train-rmse:0.406832+0.000564 test-rmse:0.424256+0.003678
## [1241] train-rmse:0.406822+0.000559 test-rmse:0.424252+0.003682
## [1242] train-rmse:0.406815+0.000561 test-rmse:0.424254+0.003682
## [1243] train-rmse:0.406806+0.000560 test-rmse:0.424256+0.003682
## [1244] train-rmse:0.406796+0.000561 test-rmse:0.424256+0.003682
## [1245] train-rmse:0.406785+0.000561 test-rmse:0.424256+0.003684
## [1246] train-rmse:0.406778+0.000563 test-rmse:0.424257+0.003685
## [1247] train-rmse:0.406770+0.000564 test-rmse:0.424260+0.003684
## [1248] train-rmse:0.406760+0.000565 test-rmse:0.424258+0.003682
## [1249] train-rmse:0.406749+0.000568 test-rmse:0.424258+0.003682
## [1250] train-rmse:0.406741+0.000570 test-rmse:0.424259+0.003681
## [1251] train-rmse:0.406731+0.000569 test-rmse:0.424259+0.003681
## [1252] train-rmse:0.406720+0.000569 test-rmse:0.424260+0.003685
## [1253] train-rmse:0.406707+0.000568 test-rmse:0.424261+0.003682
## [1254] train-rmse:0.406697+0.000567 test-rmse:0.424262+0.003681
## [1255] train-rmse:0.406685+0.000567 test-rmse:0.424261+0.003679
## [1256] train-rmse:0.406677+0.000568 test-rmse:0.424263+0.003682
## [1257] train-rmse:0.406666+0.000568 test-rmse:0.424262+0.003683
## [1258] train-rmse:0.406657+0.000570 test-rmse:0.424264+0.003683
## [1259] train-rmse:0.406647+0.000570 test-rmse:0.424265+0.003683
## [1260] train-rmse:0.406634+0.000571 test-rmse:0.424268+0.003683
## [1261] train-rmse:0.406621+0.000573 test-rmse:0.424270+0.003684
## [1262] train-rmse:0.406610+0.000573 test-rmse:0.424267+0.003691
## [1263] train-rmse:0.406603+0.000573 test-rmse:0.424267+0.003689
## [1264] train-rmse:0.406593+0.000574 test-rmse:0.424267+0.003687
## [1265] train-rmse:0.406585+0.000575 test-rmse:0.424268+0.003689
## [1266] train-rmse:0.406571+0.000577 test-rmse:0.424268+0.003688
## [1267] train-rmse:0.406561+0.000578 test-rmse:0.424269+0.003688
## [1268] train-rmse:0.406552+0.000579 test-rmse:0.424271+0.003690
## [1269] train-rmse:0.406544+0.000580 test-rmse:0.424273+0.003692
## [1270] train-rmse:0.406532+0.000581 test-rmse:0.424275+0.003696
## [1271] train-rmse:0.406526+0.000579 test-rmse:0.424273+0.003697
## [1272] train-rmse:0.406514+0.000578 test-rmse:0.424273+0.003696
## [1273] train-rmse:0.406503+0.000579 test-rmse:0.424272+0.003695
## [1274] train-rmse:0.406494+0.000577 test-rmse:0.424272+0.003697
## [1275] train-rmse:0.406485+0.000578 test-rmse:0.424275+0.003697
## [1276] train-rmse:0.406473+0.000575 test-rmse:0.424278+0.003696
## [1277] train-rmse:0.406463+0.000573 test-rmse:0.424281+0.003695
## [1278] train-rmse:0.406453+0.000576 test-rmse:0.424279+0.003694
## [1279] train-rmse:0.406442+0.000579 test-rmse:0.424281+0.003696
## [1280] train-rmse:0.406430+0.000579 test-rmse:0.424278+0.003692
## [1281] train-rmse:0.406419+0.000581 test-rmse:0.424281+0.003693
## [1282] train-rmse:0.406409+0.000581 test-rmse:0.424283+0.003693
## [1283] train-rmse:0.406396+0.000580 test-rmse:0.424285+0.003692
## [1284] train-rmse:0.406384+0.000577 test-rmse:0.424284+0.003695
## [1285] train-rmse:0.406372+0.000579 test-rmse:0.424283+0.003695
## [1286] train-rmse:0.406362+0.000582 test-rmse:0.424286+0.003697
## [1287] train-rmse:0.406351+0.000580 test-rmse:0.424287+0.003694
## [1288] train-rmse:0.406341+0.000584 test-rmse:0.424286+0.003693
## [1289] train-rmse:0.406330+0.000583 test-rmse:0.424286+0.003690
## [1290] train-rmse:0.406320+0.000589 test-rmse:0.424287+0.003688
## [1291] train-rmse:0.406310+0.000591 test-rmse:0.424289+0.003690
## [1292] train-rmse:0.406302+0.000591 test-rmse:0.424289+0.003692
## [1293] train-rmse:0.406291+0.000593 test-rmse:0.424288+0.003692
## [1294] train-rmse:0.406280+0.000592 test-rmse:0.424287+0.003688
## [1295] train-rmse:0.406267+0.000590 test-rmse:0.424285+0.003689
## [1296] train-rmse:0.406258+0.000588 test-rmse:0.424285+0.003689
## [1297] train-rmse:0.406249+0.000588 test-rmse:0.424287+0.003689
## [1298] train-rmse:0.406238+0.000586 test-rmse:0.424289+0.003687
## [1299] train-rmse:0.406225+0.000587 test-rmse:0.424286+0.003690
## [1300] train-rmse:0.406211+0.000588 test-rmse:0.424287+0.003689
## [1301] train-rmse:0.406202+0.000588 test-rmse:0.424286+0.003689
## [1302] train-rmse:0.406191+0.000588 test-rmse:0.424288+0.003689
## [1303] train-rmse:0.406182+0.000592 test-rmse:0.424290+0.003690
## [1304] train-rmse:0.406174+0.000591 test-rmse:0.424290+0.003691
## [1305] train-rmse:0.406166+0.000595 test-rmse:0.424288+0.003691
## [1306] train-rmse:0.406158+0.000595 test-rmse:0.424286+0.003690
## [1307] train-rmse:0.406149+0.000598 test-rmse:0.424288+0.003691
## [1308] train-rmse:0.406140+0.000600 test-rmse:0.424287+0.003692
## [1309] train-rmse:0.406131+0.000598 test-rmse:0.424289+0.003691
## [1310] train-rmse:0.406122+0.000597 test-rmse:0.424288+0.003692
## [1311] train-rmse:0.406112+0.000599 test-rmse:0.424288+0.003692
## [1312] train-rmse:0.406103+0.000602 test-rmse:0.424287+0.003692
## [1313] train-rmse:0.406091+0.000602 test-rmse:0.424287+0.003690
## [1314] train-rmse:0.406078+0.000604 test-rmse:0.424287+0.003688
## [1315] train-rmse:0.406073+0.000604 test-rmse:0.424288+0.003688
## [1316] train-rmse:0.406062+0.000606 test-rmse:0.424287+0.003688
## [1317] train-rmse:0.406052+0.000606 test-rmse:0.424289+0.003685
## [1318] train-rmse:0.406046+0.000609 test-rmse:0.424289+0.003685
## [1319] train-rmse:0.406040+0.000611 test-rmse:0.424288+0.003686
## [1320] train-rmse:0.406033+0.000612 test-rmse:0.424289+0.003687
## [1321] train-rmse:0.406025+0.000613 test-rmse:0.424291+0.003684
## [1322] train-rmse:0.406018+0.000615 test-rmse:0.424291+0.003684
## [1323] train-rmse:0.406010+0.000616 test-rmse:0.424293+0.003687
## [1324] train-rmse:0.406001+0.000621 test-rmse:0.424291+0.003687
## [1325] train-rmse:0.405993+0.000622 test-rmse:0.424292+0.003685
## [1326] train-rmse:0.405985+0.000628 test-rmse:0.424291+0.003685
## [1327] train-rmse:0.405977+0.000628 test-rmse:0.424289+0.003683
## [1328] train-rmse:0.405970+0.000631 test-rmse:0.424291+0.003684
## [1329] train-rmse:0.405959+0.000633 test-rmse:0.424292+0.003686
## [1330] train-rmse:0.405953+0.000634 test-rmse:0.424294+0.003688
## [1331] train-rmse:0.405942+0.000633 test-rmse:0.424296+0.003688
## [1332] train-rmse:0.405931+0.000633 test-rmse:0.424296+0.003683
## [1333] train-rmse:0.405923+0.000635 test-rmse:0.424298+0.003684
## [1334] train-rmse:0.405913+0.000636 test-rmse:0.424298+0.003683
## [1335] train-rmse:0.405904+0.000637 test-rmse:0.424299+0.003680
## [1336] train-rmse:0.405895+0.000636 test-rmse:0.424299+0.003678
## [1337] train-rmse:0.405884+0.000640 test-rmse:0.424300+0.003677
## [1338] train-rmse:0.405872+0.000642 test-rmse:0.424303+0.003678
## [1339] train-rmse:0.405861+0.000643 test-rmse:0.424303+0.003678
## [1340] train-rmse:0.405850+0.000643 test-rmse:0.424305+0.003681
## [1341] train-rmse:0.405839+0.000644 test-rmse:0.424309+0.003681
## [1342] train-rmse:0.405830+0.000647 test-rmse:0.424310+0.003681
## [1343] train-rmse:0.405819+0.000648 test-rmse:0.424312+0.003678
## [1344] train-rmse:0.405811+0.000649 test-rmse:0.424309+0.003675
## [1345] train-rmse:0.405798+0.000648 test-rmse:0.424308+0.003676
## [1346] train-rmse:0.405785+0.000648 test-rmse:0.424309+0.003673
## [1347] train-rmse:0.405774+0.000651 test-rmse:0.424310+0.003671
## [1348] train-rmse:0.405761+0.000652 test-rmse:0.424309+0.003673
## [1349] train-rmse:0.405754+0.000654 test-rmse:0.424309+0.003673
## [1350] train-rmse:0.405744+0.000653 test-rmse:0.424310+0.003673
## [1351] train-rmse:0.405736+0.000652 test-rmse:0.424310+0.003673
## [1352] train-rmse:0.405727+0.000655 test-rmse:0.424310+0.003671
## [1353] train-rmse:0.405720+0.000655 test-rmse:0.424312+0.003669
## [1354] train-rmse:0.405712+0.000657 test-rmse:0.424312+0.003668
## [1355] train-rmse:0.405704+0.000657 test-rmse:0.424314+0.003669
## [1356] train-rmse:0.405701+0.000657 test-rmse:0.424316+0.003667
## [1357] train-rmse:0.405691+0.000658 test-rmse:0.424317+0.003666
## [1358] train-rmse:0.405683+0.000662 test-rmse:0.424317+0.003670
## [1359] train-rmse:0.405673+0.000663 test-rmse:0.424320+0.003673
## [1360] train-rmse:0.405662+0.000665 test-rmse:0.424319+0.003672
## [1361] train-rmse:0.405651+0.000665 test-rmse:0.424321+0.003673
## [1362] train-rmse:0.405643+0.000668 test-rmse:0.424321+0.003672
## [1363] train-rmse:0.405633+0.000672 test-rmse:0.424322+0.003671
## [1364] train-rmse:0.405625+0.000673 test-rmse:0.424322+0.003673
## [1365] train-rmse:0.405614+0.000676 test-rmse:0.424326+0.003673
## [1366] train-rmse:0.405603+0.000676 test-rmse:0.424328+0.003672
## [1367] train-rmse:0.405592+0.000678 test-rmse:0.424331+0.003674
## [1368] train-rmse:0.405584+0.000678 test-rmse:0.424332+0.003673
## [1369] train-rmse:0.405574+0.000681 test-rmse:0.424335+0.003674
## [1370] train-rmse:0.405564+0.000684 test-rmse:0.424336+0.003676
## [1371] train-rmse:0.405552+0.000680 test-rmse:0.424338+0.003677
## [1372] train-rmse:0.405539+0.000680 test-rmse:0.424338+0.003677
## [1373] train-rmse:0.405529+0.000683 test-rmse:0.424336+0.003676
## [1374] train-rmse:0.405518+0.000685 test-rmse:0.424337+0.003675
## [1375] train-rmse:0.405510+0.000686 test-rmse:0.424339+0.003676
## [1376] train-rmse:0.405499+0.000684 test-rmse:0.424340+0.003676
## [1377] train-rmse:0.405486+0.000687 test-rmse:0.424339+0.003675
## [1378] train-rmse:0.405476+0.000685 test-rmse:0.424342+0.003677
## [1379] train-rmse:0.405465+0.000686 test-rmse:0.424341+0.003677
## [1380] train-rmse:0.405458+0.000686 test-rmse:0.424341+0.003678
## [1381] train-rmse:0.405451+0.000686 test-rmse:0.424344+0.003677
## [1382] train-rmse:0.405444+0.000690 test-rmse:0.424347+0.003677
## [1383] train-rmse:0.405433+0.000690 test-rmse:0.424346+0.003677
## [1384] train-rmse:0.405422+0.000689 test-rmse:0.424349+0.003678
## [1385] train-rmse:0.405413+0.000691 test-rmse:0.424348+0.003677
## [1386] train-rmse:0.405401+0.000692 test-rmse:0.424346+0.003676
## [1387] train-rmse:0.405392+0.000690 test-rmse:0.424351+0.003675
## [1388] train-rmse:0.405382+0.000690 test-rmse:0.424352+0.003673
## [1389] train-rmse:0.405371+0.000690 test-rmse:0.424349+0.003671
## [1390] train-rmse:0.405362+0.000689 test-rmse:0.424351+0.003671
## [1391] train-rmse:0.405353+0.000691 test-rmse:0.424352+0.003673
## [1392] train-rmse:0.405341+0.000689 test-rmse:0.424353+0.003669
## [1393] train-rmse:0.405331+0.000688 test-rmse:0.424353+0.003666
## [1394] train-rmse:0.405320+0.000687 test-rmse:0.424354+0.003668
## [1395] train-rmse:0.405311+0.000690 test-rmse:0.424354+0.003667
## [1396] train-rmse:0.405303+0.000690 test-rmse:0.424352+0.003666
## [1397] train-rmse:0.405293+0.000693 test-rmse:0.424352+0.003667
## [1398] train-rmse:0.405283+0.000696 test-rmse:0.424351+0.003666
## [1399] train-rmse:0.405274+0.000698 test-rmse:0.424349+0.003664
## [1400] train-rmse:0.405267+0.000700 test-rmse:0.424349+0.003665
## [1401] train-rmse:0.405260+0.000701 test-rmse:0.424350+0.003665
## [1402] train-rmse:0.405252+0.000701 test-rmse:0.424351+0.003663
## [1403] train-rmse:0.405243+0.000699 test-rmse:0.424352+0.003662
## [1404] train-rmse:0.405234+0.000703 test-rmse:0.424356+0.003661
## [1405] train-rmse:0.405224+0.000706 test-rmse:0.424355+0.003658
## [1406] train-rmse:0.405215+0.000706 test-rmse:0.424355+0.003655
## [1407] train-rmse:0.405206+0.000708 test-rmse:0.424356+0.003657
## [1408] train-rmse:0.405197+0.000709 test-rmse:0.424356+0.003657
## [1409] train-rmse:0.405192+0.000711 test-rmse:0.424357+0.003654
## [1410] train-rmse:0.405186+0.000712 test-rmse:0.424357+0.003653
## [1411] train-rmse:0.405175+0.000712 test-rmse:0.424358+0.003653
## [1412] train-rmse:0.405168+0.000713 test-rmse:0.424356+0.003652
## [1413] train-rmse:0.405157+0.000717 test-rmse:0.424356+0.003654
## [1414] train-rmse:0.405149+0.000716 test-rmse:0.424357+0.003656
## [1415] train-rmse:0.405141+0.000718 test-rmse:0.424358+0.003656
## [1416] train-rmse:0.405135+0.000719 test-rmse:0.424358+0.003655
## [1417] train-rmse:0.405125+0.000717 test-rmse:0.424361+0.003657
## [1418] train-rmse:0.405115+0.000715 test-rmse:0.424362+0.003657
## [1419] train-rmse:0.405107+0.000719 test-rmse:0.424363+0.003658
## [1420] train-rmse:0.405098+0.000721 test-rmse:0.424363+0.003656
## [1421] train-rmse:0.405086+0.000724 test-rmse:0.424365+0.003657
## [1422] train-rmse:0.405074+0.000727 test-rmse:0.424366+0.003653
## [1423] train-rmse:0.405064+0.000729 test-rmse:0.424365+0.003654
## [1424] train-rmse:0.405054+0.000729 test-rmse:0.424367+0.003657
## [1425] train-rmse:0.405045+0.000728 test-rmse:0.424368+0.003658
## [1426] train-rmse:0.405035+0.000730 test-rmse:0.424370+0.003659
## [1427] train-rmse:0.405027+0.000732 test-rmse:0.424371+0.003657
## [1428] train-rmse:0.405019+0.000736 test-rmse:0.424372+0.003658
## [1429] train-rmse:0.405008+0.000736 test-rmse:0.424372+0.003660
## [1430] train-rmse:0.404998+0.000737 test-rmse:0.424372+0.003660
## [1431] train-rmse:0.404989+0.000737 test-rmse:0.424373+0.003660
## [1432] train-rmse:0.404977+0.000735 test-rmse:0.424372+0.003661
## [1433] train-rmse:0.404968+0.000733 test-rmse:0.424373+0.003660
## [1434] train-rmse:0.404960+0.000736 test-rmse:0.424373+0.003660
## [1435] train-rmse:0.404950+0.000737 test-rmse:0.424373+0.003660
## [1436] train-rmse:0.404938+0.000736 test-rmse:0.424372+0.003661
## [1437] train-rmse:0.404932+0.000740 test-rmse:0.424374+0.003661
## [1438] train-rmse:0.404923+0.000741 test-rmse:0.424374+0.003661
## [1439] train-rmse:0.404916+0.000741 test-rmse:0.424377+0.003663
## [1440] train-rmse:0.404907+0.000742 test-rmse:0.424377+0.003665
## [1441] train-rmse:0.404898+0.000747 test-rmse:0.424378+0.003664
## [1442] train-rmse:0.404890+0.000746 test-rmse:0.424378+0.003664
## [1443] train-rmse:0.404881+0.000746 test-rmse:0.424380+0.003663
## [1444] train-rmse:0.404871+0.000746 test-rmse:0.424382+0.003664
## [1445] train-rmse:0.404864+0.000746 test-rmse:0.424384+0.003663
## [1446] train-rmse:0.404853+0.000747 test-rmse:0.424387+0.003664
## [1447] train-rmse:0.404845+0.000746 test-rmse:0.424388+0.003664
## [1448] train-rmse:0.404836+0.000744 test-rmse:0.424388+0.003665
## [1449] train-rmse:0.404823+0.000746 test-rmse:0.424390+0.003668
## [1450] train-rmse:0.404815+0.000747 test-rmse:0.424391+0.003670
## [1451] train-rmse:0.404806+0.000748 test-rmse:0.424392+0.003670
## [1452] train-rmse:0.404797+0.000749 test-rmse:0.424391+0.003667
## [1453] train-rmse:0.404789+0.000750 test-rmse:0.424389+0.003667
## [1454] train-rmse:0.404779+0.000753 test-rmse:0.424390+0.003667
## [1455] train-rmse:0.404771+0.000757 test-rmse:0.424391+0.003669
## [1456] train-rmse:0.404762+0.000758 test-rmse:0.424392+0.003670
## [1457] train-rmse:0.404753+0.000759 test-rmse:0.424393+0.003668
## [1458] train-rmse:0.404744+0.000758 test-rmse:0.424395+0.003666
## [1459] train-rmse:0.404735+0.000760 test-rmse:0.424395+0.003667
## [1460] train-rmse:0.404725+0.000758 test-rmse:0.424393+0.003669
## [1461] train-rmse:0.404717+0.000759 test-rmse:0.424395+0.003670
## [1462] train-rmse:0.404707+0.000764 test-rmse:0.424396+0.003671
## [1463] train-rmse:0.404701+0.000765 test-rmse:0.424395+0.003672
## [1464] train-rmse:0.404691+0.000763 test-rmse:0.424398+0.003668
## [1465] train-rmse:0.404681+0.000764 test-rmse:0.424398+0.003669
## [1466] train-rmse:0.404672+0.000767 test-rmse:0.424399+0.003669
## [1467] train-rmse:0.404661+0.000767 test-rmse:0.424400+0.003670
## [1468] train-rmse:0.404655+0.000768 test-rmse:0.424399+0.003667
## [1469] train-rmse:0.404646+0.000768 test-rmse:0.424399+0.003666
## [1470] train-rmse:0.404636+0.000770 test-rmse:0.424400+0.003667
## [1471] train-rmse:0.404629+0.000772 test-rmse:0.424400+0.003668
## [1472] train-rmse:0.404618+0.000773 test-rmse:0.424399+0.003669
## [1473] train-rmse:0.404607+0.000775 test-rmse:0.424400+0.003668
## [1474] train-rmse:0.404598+0.000775 test-rmse:0.424400+0.003667
## [1475] train-rmse:0.404588+0.000774 test-rmse:0.424399+0.003666
## [1476] train-rmse:0.404579+0.000774 test-rmse:0.424401+0.003667
## [1477] train-rmse:0.404568+0.000776 test-rmse:0.424402+0.003668
## [1478] train-rmse:0.404561+0.000778 test-rmse:0.424403+0.003668
## [1479] train-rmse:0.404551+0.000779 test-rmse:0.424403+0.003670
## [1480] train-rmse:0.404542+0.000782 test-rmse:0.424405+0.003669
## [1481] train-rmse:0.404532+0.000782 test-rmse:0.424406+0.003669
## [1482] train-rmse:0.404524+0.000779 test-rmse:0.424406+0.003670
## [1483] train-rmse:0.404517+0.000778 test-rmse:0.424406+0.003670
## [1484] train-rmse:0.404510+0.000778 test-rmse:0.424405+0.003671
## [1485] train-rmse:0.404498+0.000778 test-rmse:0.424406+0.003668
## [1486] train-rmse:0.404489+0.000781 test-rmse:0.424407+0.003670
## [1487] train-rmse:0.404480+0.000779 test-rmse:0.424408+0.003670
## [1488] train-rmse:0.404472+0.000778 test-rmse:0.424407+0.003670
## [1489] train-rmse:0.404463+0.000779 test-rmse:0.424407+0.003669
## [1490] train-rmse:0.404454+0.000781 test-rmse:0.424405+0.003667
## [1491] train-rmse:0.404445+0.000781 test-rmse:0.424406+0.003667
## [1492] train-rmse:0.404436+0.000781 test-rmse:0.424408+0.003667
## [1493] train-rmse:0.404426+0.000780 test-rmse:0.424408+0.003668
## [1494] train-rmse:0.404415+0.000782 test-rmse:0.424411+0.003667
## [1495] train-rmse:0.404409+0.000783 test-rmse:0.424410+0.003667
## [1496] train-rmse:0.404401+0.000786 test-rmse:0.424411+0.003667
## [1497] train-rmse:0.404388+0.000785 test-rmse:0.424410+0.003665
## [1498] train-rmse:0.404379+0.000783 test-rmse:0.424411+0.003665
## [1499] train-rmse:0.404371+0.000784 test-rmse:0.424412+0.003664
## [1500] train-rmse:0.404359+0.000782 test-rmse:0.424413+0.003665
## [1501] train-rmse:0.404350+0.000786 test-rmse:0.424415+0.003665
## [1502] train-rmse:0.404338+0.000787 test-rmse:0.424416+0.003664
## [1503] train-rmse:0.404326+0.000788 test-rmse:0.424416+0.003662
## [1504] train-rmse:0.404313+0.000790 test-rmse:0.424419+0.003662
## [1505] train-rmse:0.404301+0.000790 test-rmse:0.424421+0.003661
## [1506] train-rmse:0.404294+0.000792 test-rmse:0.424421+0.003661
## [1507] train-rmse:0.404286+0.000791 test-rmse:0.424418+0.003663
## [1508] train-rmse:0.404277+0.000789 test-rmse:0.424419+0.003662
## [1509] train-rmse:0.404268+0.000789 test-rmse:0.424414+0.003665
## [1510] train-rmse:0.404258+0.000792 test-rmse:0.424415+0.003666
## [1511] train-rmse:0.404250+0.000792 test-rmse:0.424417+0.003666
## [1512] train-rmse:0.404240+0.000794 test-rmse:0.424415+0.003664
## [1513] train-rmse:0.404231+0.000794 test-rmse:0.424416+0.003665
## [1514] train-rmse:0.404221+0.000792 test-rmse:0.424420+0.003661
## [1515] train-rmse:0.404212+0.000793 test-rmse:0.424423+0.003664
## [1516] train-rmse:0.404204+0.000796 test-rmse:0.424423+0.003665
## [1517] train-rmse:0.404196+0.000793 test-rmse:0.424424+0.003664
## [1518] train-rmse:0.404187+0.000796 test-rmse:0.424424+0.003665
## [1519] train-rmse:0.404178+0.000797 test-rmse:0.424424+0.003666
## [1520] train-rmse:0.404170+0.000800 test-rmse:0.424424+0.003665
## [1521] train-rmse:0.404163+0.000802 test-rmse:0.424423+0.003665
## [1522] train-rmse:0.404150+0.000806 test-rmse:0.424423+0.003665
## [1523] train-rmse:0.404141+0.000809 test-rmse:0.424424+0.003664
## [1524] train-rmse:0.404133+0.000811 test-rmse:0.424425+0.003660
## [1525] train-rmse:0.404126+0.000812 test-rmse:0.424426+0.003661
## [1526] train-rmse:0.404119+0.000813 test-rmse:0.424426+0.003661
## [1527] train-rmse:0.404109+0.000815 test-rmse:0.424424+0.003662
## [1528] train-rmse:0.404098+0.000817 test-rmse:0.424426+0.003660
## [1529] train-rmse:0.404090+0.000817 test-rmse:0.424427+0.003660
## [1530] train-rmse:0.404082+0.000818 test-rmse:0.424426+0.003659
## [1531] train-rmse:0.404074+0.000821 test-rmse:0.424428+0.003662
## [1532] train-rmse:0.404065+0.000822 test-rmse:0.424428+0.003663
## [1533] train-rmse:0.404058+0.000824 test-rmse:0.424428+0.003663
## [1534] train-rmse:0.404048+0.000823 test-rmse:0.424426+0.003665
## [1535] train-rmse:0.404038+0.000826 test-rmse:0.424430+0.003663
## [1536] train-rmse:0.404027+0.000826 test-rmse:0.424433+0.003661
## [1537] train-rmse:0.404015+0.000823 test-rmse:0.424433+0.003663
## [1538] train-rmse:0.404004+0.000823 test-rmse:0.424431+0.003664
## [1539] train-rmse:0.403994+0.000821 test-rmse:0.424430+0.003664
## [1540] train-rmse:0.403985+0.000826 test-rmse:0.424429+0.003666
## [1541] train-rmse:0.403973+0.000824 test-rmse:0.424430+0.003666
## [1542] train-rmse:0.403960+0.000821 test-rmse:0.424429+0.003664
## [1543] train-rmse:0.403951+0.000821 test-rmse:0.424431+0.003663
## [1544] train-rmse:0.403938+0.000819 test-rmse:0.424431+0.003662
## [1545] train-rmse:0.403930+0.000821 test-rmse:0.424430+0.003660
## [1546] train-rmse:0.403917+0.000823 test-rmse:0.424429+0.003659
## [1547] train-rmse:0.403910+0.000823 test-rmse:0.424429+0.003659
## [1548] train-rmse:0.403898+0.000821 test-rmse:0.424430+0.003657
## [1549] train-rmse:0.403886+0.000822 test-rmse:0.424432+0.003658
## [1550] train-rmse:0.403878+0.000825 test-rmse:0.424432+0.003659
## [1551] train-rmse:0.403865+0.000826 test-rmse:0.424433+0.003653
## [1552] train-rmse:0.403857+0.000825 test-rmse:0.424434+0.003654
## [1553] train-rmse:0.403849+0.000826 test-rmse:0.424434+0.003654
## [1554] train-rmse:0.403839+0.000829 test-rmse:0.424437+0.003654
## [1555] train-rmse:0.403829+0.000829 test-rmse:0.424440+0.003652
## [1556] train-rmse:0.403819+0.000830 test-rmse:0.424440+0.003650
## [1557] train-rmse:0.403810+0.000833 test-rmse:0.424441+0.003648
## [1558] train-rmse:0.403800+0.000837 test-rmse:0.424441+0.003649
## [1559] train-rmse:0.403787+0.000839 test-rmse:0.424441+0.003650
## [1560] train-rmse:0.403774+0.000840 test-rmse:0.424442+0.003648
## [1561] train-rmse:0.403768+0.000843 test-rmse:0.424443+0.003648
## [1562] train-rmse:0.403760+0.000842 test-rmse:0.424446+0.003647
## [1563] train-rmse:0.403752+0.000847 test-rmse:0.424446+0.003649
## [1564] train-rmse:0.403740+0.000844 test-rmse:0.424447+0.003647
## [1565] train-rmse:0.403732+0.000845 test-rmse:0.424447+0.003646
## [1566] train-rmse:0.403723+0.000847 test-rmse:0.424448+0.003645
## [1567] train-rmse:0.403713+0.000851 test-rmse:0.424448+0.003642
## [1568] train-rmse:0.403705+0.000851 test-rmse:0.424449+0.003642
## [1569] train-rmse:0.403694+0.000851 test-rmse:0.424448+0.003639
## [1570] train-rmse:0.403686+0.000854 test-rmse:0.424451+0.003641
## [1571] train-rmse:0.403676+0.000856 test-rmse:0.424451+0.003640
## [1572] train-rmse:0.403669+0.000857 test-rmse:0.424450+0.003641
## [1573] train-rmse:0.403659+0.000853 test-rmse:0.424446+0.003641
## [1574] train-rmse:0.403652+0.000856 test-rmse:0.424446+0.003641
## [1575] train-rmse:0.403642+0.000859 test-rmse:0.424446+0.003640
## [1576] train-rmse:0.403632+0.000863 test-rmse:0.424448+0.003639
## [1577] train-rmse:0.403622+0.000866 test-rmse:0.424446+0.003639
## [1578] train-rmse:0.403611+0.000869 test-rmse:0.424448+0.003641
## [1579] train-rmse:0.403601+0.000873 test-rmse:0.424448+0.003643
## [1580] train-rmse:0.403590+0.000872 test-rmse:0.424449+0.003641
## [1581] train-rmse:0.403582+0.000874 test-rmse:0.424449+0.003642
## [1582] train-rmse:0.403572+0.000879 test-rmse:0.424449+0.003641
## [1583] train-rmse:0.403561+0.000878 test-rmse:0.424451+0.003641
## [1584] train-rmse:0.403555+0.000882 test-rmse:0.424450+0.003640
## [1585] train-rmse:0.403547+0.000885 test-rmse:0.424451+0.003639
## [1586] train-rmse:0.403539+0.000889 test-rmse:0.424451+0.003640
## [1587] train-rmse:0.403532+0.000892 test-rmse:0.424453+0.003642
## [1588] train-rmse:0.403522+0.000895 test-rmse:0.424454+0.003642
## [1589] train-rmse:0.403512+0.000898 test-rmse:0.424455+0.003642
## [1590] train-rmse:0.403504+0.000899 test-rmse:0.424456+0.003643
## [1591] train-rmse:0.403496+0.000902 test-rmse:0.424453+0.003639
## [1592] train-rmse:0.403488+0.000903 test-rmse:0.424455+0.003640
## [1593] train-rmse:0.403480+0.000906 test-rmse:0.424455+0.003638
## [1594] train-rmse:0.403474+0.000907 test-rmse:0.424456+0.003639
## [1595] train-rmse:0.403466+0.000907 test-rmse:0.424460+0.003639
## [1596] train-rmse:0.403454+0.000912 test-rmse:0.424458+0.003639
## [1597] train-rmse:0.403447+0.000914 test-rmse:0.424458+0.003638
## [1598] train-rmse:0.403438+0.000917 test-rmse:0.424460+0.003637
## [1599] train-rmse:0.403429+0.000920 test-rmse:0.424460+0.003635
## [1600] train-rmse:0.403420+0.000920 test-rmse:0.424459+0.003634
## [1601] train-rmse:0.403412+0.000924 test-rmse:0.424461+0.003636
## [1602] train-rmse:0.403406+0.000926 test-rmse:0.424462+0.003637
## [1603] train-rmse:0.403396+0.000930 test-rmse:0.424462+0.003637
## [1604] train-rmse:0.403391+0.000932 test-rmse:0.424465+0.003637
## [1605] train-rmse:0.403383+0.000933 test-rmse:0.424463+0.003636
## [1606] train-rmse:0.403375+0.000935 test-rmse:0.424465+0.003635
## [1607] train-rmse:0.403363+0.000934 test-rmse:0.424468+0.003635
## [1608] train-rmse:0.403357+0.000937 test-rmse:0.424468+0.003636
## [1609] train-rmse:0.403347+0.000939 test-rmse:0.424475+0.003636
## [1610] train-rmse:0.403339+0.000942 test-rmse:0.424477+0.003636
## [1611] train-rmse:0.403328+0.000944 test-rmse:0.424476+0.003635
## [1612] train-rmse:0.403322+0.000947 test-rmse:0.424476+0.003636
## [1613] train-rmse:0.403316+0.000950 test-rmse:0.424480+0.003637
## [1614] train-rmse:0.403307+0.000953 test-rmse:0.424479+0.003637
## [1615] train-rmse:0.403298+0.000954 test-rmse:0.424479+0.003636
## [1616] train-rmse:0.403290+0.000959 test-rmse:0.424480+0.003637
## [1617] train-rmse:0.403283+0.000962 test-rmse:0.424481+0.003638
## [1618] train-rmse:0.403276+0.000964 test-rmse:0.424479+0.003637
## [1619] train-rmse:0.403269+0.000969 test-rmse:0.424479+0.003637
## [1620] train-rmse:0.403260+0.000971 test-rmse:0.424481+0.003637
## [1621] train-rmse:0.403254+0.000973 test-rmse:0.424481+0.003637
## [1622] train-rmse:0.403247+0.000977 test-rmse:0.424485+0.003638
## [1623] train-rmse:0.403240+0.000978 test-rmse:0.424487+0.003641
## [1624] train-rmse:0.403233+0.000980 test-rmse:0.424488+0.003641
## [1625] train-rmse:0.403228+0.000982 test-rmse:0.424491+0.003645
## [1626] train-rmse:0.403220+0.000982 test-rmse:0.424492+0.003644
## [1627] train-rmse:0.403213+0.000986 test-rmse:0.424490+0.003644
## [1628] train-rmse:0.403206+0.000988 test-rmse:0.424491+0.003646
## [1629] train-rmse:0.403201+0.000990 test-rmse:0.424489+0.003645
## [1630] train-rmse:0.403195+0.000993 test-rmse:0.424488+0.003647
## [1631] train-rmse:0.403186+0.000993 test-rmse:0.424490+0.003646
## [1632] train-rmse:0.403181+0.000993 test-rmse:0.424491+0.003643
## [1633] train-rmse:0.403170+0.000996 test-rmse:0.424488+0.003644
## [1634] train-rmse:0.403161+0.000998 test-rmse:0.424492+0.003647
## [1635] train-rmse:0.403153+0.000998 test-rmse:0.424494+0.003653
## [1636] train-rmse:0.403145+0.000999 test-rmse:0.424497+0.003653
## [1637] train-rmse:0.403134+0.001001 test-rmse:0.424495+0.003652
## [1638] train-rmse:0.403127+0.001001 test-rmse:0.424499+0.003652
## [1639] train-rmse:0.403120+0.001004 test-rmse:0.424501+0.003653
## [1640] train-rmse:0.403114+0.001007 test-rmse:0.424500+0.003653
## [1641] train-rmse:0.403106+0.001007 test-rmse:0.424500+0.003652
## [1642] train-rmse:0.403100+0.001009 test-rmse:0.424502+0.003652
## [1643] train-rmse:0.403091+0.001010 test-rmse:0.424505+0.003653
## [1644] train-rmse:0.403083+0.001014 test-rmse:0.424507+0.003654
## [1645] train-rmse:0.403076+0.001016 test-rmse:0.424506+0.003654
## [1646] train-rmse:0.403070+0.001017 test-rmse:0.424506+0.003653
## [1647] train-rmse:0.403064+0.001020 test-rmse:0.424508+0.003654
## [1648] train-rmse:0.403054+0.001023 test-rmse:0.424508+0.003652
## [1649] train-rmse:0.403047+0.001025 test-rmse:0.424507+0.003652
## [1650] train-rmse:0.403038+0.001028 test-rmse:0.424509+0.003652
## [1651] train-rmse:0.403027+0.001030 test-rmse:0.424510+0.003652
## [1652] train-rmse:0.403019+0.001032 test-rmse:0.424510+0.003654
## [1653] train-rmse:0.403013+0.001032 test-rmse:0.424513+0.003652
## [1654] train-rmse:0.403006+0.001033 test-rmse:0.424513+0.003651
## [1655] train-rmse:0.402996+0.001036 test-rmse:0.424515+0.003652
## [1656] train-rmse:0.402987+0.001037 test-rmse:0.424519+0.003652
## [1657] train-rmse:0.402979+0.001038 test-rmse:0.424520+0.003652
## [1658] train-rmse:0.402969+0.001039 test-rmse:0.424521+0.003654
## [1659] train-rmse:0.402961+0.001041 test-rmse:0.424522+0.003656
## [1660] train-rmse:0.402953+0.001042 test-rmse:0.424523+0.003654
## [1661] train-rmse:0.402948+0.001045 test-rmse:0.424524+0.003651
## [1662] train-rmse:0.402941+0.001045 test-rmse:0.424524+0.003651
## [1663] train-rmse:0.402936+0.001047 test-rmse:0.424525+0.003649
## [1664] train-rmse:0.402925+0.001050 test-rmse:0.424524+0.003651
## [1665] train-rmse:0.402916+0.001052 test-rmse:0.424524+0.003650
## [1666] train-rmse:0.402911+0.001055 test-rmse:0.424525+0.003650
## [1667] train-rmse:0.402901+0.001056 test-rmse:0.424526+0.003650
## [1668] train-rmse:0.402892+0.001058 test-rmse:0.424524+0.003650
## [1669] train-rmse:0.402884+0.001062 test-rmse:0.424528+0.003650
## [1670] train-rmse:0.402878+0.001065 test-rmse:0.424529+0.003652
## [1671] train-rmse:0.402871+0.001065 test-rmse:0.424529+0.003651
## [1672] train-rmse:0.402863+0.001066 test-rmse:0.424532+0.003651
## [1673] train-rmse:0.402857+0.001067 test-rmse:0.424531+0.003650
## [1674] train-rmse:0.402847+0.001068 test-rmse:0.424533+0.003650
## [1675] train-rmse:0.402838+0.001070 test-rmse:0.424534+0.003648
## [1676] train-rmse:0.402830+0.001070 test-rmse:0.424538+0.003646
## [1677] train-rmse:0.402821+0.001071 test-rmse:0.424539+0.003647
## [1678] train-rmse:0.402814+0.001071 test-rmse:0.424542+0.003646
## [1679] train-rmse:0.402803+0.001072 test-rmse:0.424544+0.003646
## [1680] train-rmse:0.402794+0.001075 test-rmse:0.424545+0.003648
## [1681] train-rmse:0.402784+0.001077 test-rmse:0.424544+0.003647
## [1682] train-rmse:0.402778+0.001079 test-rmse:0.424545+0.003647
## [1683] train-rmse:0.402769+0.001079 test-rmse:0.424545+0.003645
## [1684] train-rmse:0.402762+0.001077 test-rmse:0.424545+0.003646
## [1685] train-rmse:0.402754+0.001078 test-rmse:0.424546+0.003644
## [1686] train-rmse:0.402747+0.001081 test-rmse:0.424548+0.003642
## [1687] train-rmse:0.402738+0.001082 test-rmse:0.424549+0.003644
## [1688] train-rmse:0.402729+0.001082 test-rmse:0.424550+0.003644
## [1689] train-rmse:0.402723+0.001082 test-rmse:0.424552+0.003644
## [1690] train-rmse:0.402714+0.001083 test-rmse:0.424550+0.003647
## [1691] train-rmse:0.402704+0.001086 test-rmse:0.424550+0.003647
## [1692] train-rmse:0.402696+0.001088 test-rmse:0.424553+0.003647
## [1693] train-rmse:0.402689+0.001091 test-rmse:0.424554+0.003647
## [1694] train-rmse:0.402675+0.001090 test-rmse:0.424554+0.003646
## [1695] train-rmse:0.402670+0.001094 test-rmse:0.424556+0.003646
## [1696] train-rmse:0.402662+0.001096 test-rmse:0.424556+0.003647
## [1697] train-rmse:0.402657+0.001098 test-rmse:0.424557+0.003647
## [1698] train-rmse:0.402649+0.001097 test-rmse:0.424554+0.003648
## [1699] train-rmse:0.402641+0.001097 test-rmse:0.424553+0.003647
## [1700] train-rmse:0.402634+0.001099 test-rmse:0.424554+0.003647
## [1701] train-rmse:0.402624+0.001101 test-rmse:0.424553+0.003646
## [1702] train-rmse:0.402615+0.001104 test-rmse:0.424555+0.003646
## [1703] train-rmse:0.402602+0.001104 test-rmse:0.424553+0.003646
## [1704] train-rmse:0.402592+0.001101 test-rmse:0.424554+0.003648
## [1705] train-rmse:0.402582+0.001101 test-rmse:0.424555+0.003646
## [1706] train-rmse:0.402578+0.001103 test-rmse:0.424556+0.003646
## [1707] train-rmse:0.402575+0.001103 test-rmse:0.424556+0.003645
## [1708] train-rmse:0.402565+0.001106 test-rmse:0.424559+0.003643
## [1709] train-rmse:0.402551+0.001107 test-rmse:0.424558+0.003641
## [1710] train-rmse:0.402540+0.001111 test-rmse:0.424560+0.003641
## [1711] train-rmse:0.402531+0.001115 test-rmse:0.424560+0.003642
## [1712] train-rmse:0.402524+0.001114 test-rmse:0.424561+0.003642
## [1713] train-rmse:0.402517+0.001118 test-rmse:0.424565+0.003644
## [1714] train-rmse:0.402507+0.001114 test-rmse:0.424564+0.003645
## [1715] train-rmse:0.402496+0.001114 test-rmse:0.424567+0.003647
## [1716] train-rmse:0.402487+0.001113 test-rmse:0.424568+0.003647
## [1717] train-rmse:0.402478+0.001116 test-rmse:0.424572+0.003647
## [1718] train-rmse:0.402471+0.001119 test-rmse:0.424571+0.003648
## [1719] train-rmse:0.402461+0.001119 test-rmse:0.424570+0.003645
## [1720] train-rmse:0.402452+0.001121 test-rmse:0.424571+0.003644
## [1721] train-rmse:0.402441+0.001121 test-rmse:0.424573+0.003642
## [1722] train-rmse:0.402435+0.001122 test-rmse:0.424575+0.003641
## [1723] train-rmse:0.402425+0.001126 test-rmse:0.424575+0.003642
## [1724] train-rmse:0.402412+0.001127 test-rmse:0.424579+0.003642
## [1725] train-rmse:0.402402+0.001128 test-rmse:0.424580+0.003640
## [1726] train-rmse:0.402392+0.001130 test-rmse:0.424580+0.003640
## [1727] train-rmse:0.402383+0.001130 test-rmse:0.424581+0.003639
## [1728] train-rmse:0.402375+0.001130 test-rmse:0.424584+0.003640
## [1729] train-rmse:0.402369+0.001133 test-rmse:0.424585+0.003641
## [1730] train-rmse:0.402360+0.001132 test-rmse:0.424589+0.003642
## [1731] train-rmse:0.402352+0.001135 test-rmse:0.424590+0.003641
## [1732] train-rmse:0.402343+0.001137 test-rmse:0.424589+0.003644
## [1733] train-rmse:0.402330+0.001135 test-rmse:0.424592+0.003645
## [1734] train-rmse:0.402320+0.001135 test-rmse:0.424596+0.003646
## [1735] train-rmse:0.402311+0.001137 test-rmse:0.424596+0.003647
## [1736] train-rmse:0.402305+0.001139 test-rmse:0.424597+0.003645
## [1737] train-rmse:0.402296+0.001141 test-rmse:0.424595+0.003644
## [1738] train-rmse:0.402287+0.001144 test-rmse:0.424598+0.003645
## [1739] train-rmse:0.402277+0.001147 test-rmse:0.424605+0.003646
## [1740] train-rmse:0.402267+0.001148 test-rmse:0.424605+0.003645
## [1741] train-rmse:0.402259+0.001148 test-rmse:0.424606+0.003645
## [1742] train-rmse:0.402253+0.001149 test-rmse:0.424606+0.003647
## [1743] train-rmse:0.402242+0.001148 test-rmse:0.424606+0.003648
## [1744] train-rmse:0.402233+0.001146 test-rmse:0.424608+0.003647
## [1745] train-rmse:0.402223+0.001150 test-rmse:0.424604+0.003645
## [1746] train-rmse:0.402213+0.001152 test-rmse:0.424603+0.003644
## [1747] train-rmse:0.402206+0.001151 test-rmse:0.424604+0.003644
## [1748] train-rmse:0.402201+0.001150 test-rmse:0.424606+0.003644
## [1749] train-rmse:0.402192+0.001146 test-rmse:0.424607+0.003646
## [1750] train-rmse:0.402183+0.001145 test-rmse:0.424607+0.003645xgbtrain <- xgb.train(params = xgb_params$params, data = dtrain, nrounds = xgb_params$nrounds)
feats_mat_comb_df <- feats_mat_comb_df %>%
mutate(pred = xgbcv$pred)Predict all CpGs:
d_all <- xgb.DMatrix(res %>%
mutate(score_max_a = pmax(score_a_plus, score_a_minus)) %>%
mutate(score_max_b = pmax(score_b_plus, score_b_minus)) %>%
select(score_a_plus, score_a_minus, score_b_plus, score_b_minus, score_max_a, score_max_b) %>%
as.matrix())
res <- res %>%
mutate(comb_score = predict(xgbtrain, d_all)) shap_contrib <- predict(xgbtrain, dtrain, predcontrib=TRUE, approxcontrib=TRUE)
shap_contrib %>% head()## score_a_plus score_a_minus score_b_plus score_b_minus score_max_a
## [1,] 0.03257944 0.00662072 0.13034897 -0.08361857 0.07228424
## [2,] 0.03127447 -0.07440750 -0.05122968 -0.06406826 -0.08237033
## [3,] -0.17793657 0.04980103 -0.08292633 0.03964013 -0.11479261
## [4,] -0.04900409 0.02451609 -0.07236253 -0.02743806 -0.01833785
## [5,] -0.14197846 0.01013156 -0.02193703 -0.04253504 -0.18508732
## [6,] 0.07941829 -0.02812006 -0.04589148 0.15377595 -0.04417774
## score_max_b BIAS
## [1,] 0.2976556 -0.2581046
## [2,] -0.3153226 -0.2581046
## [3,] 0.0373612 -0.2581046
## [4,] -0.2095805 -0.2581046
## [5,] -0.1496402 -0.2581046
## [6,] 0.2954025 -0.2581046## score_a_plus score_a_minus score_b_plus score_b_minus score_max_a
## 0.05898015 0.05601591 0.06136920 0.07151962 0.10136252
## score_max_b BIAS
## 0.16868949 0.258104625.1.5 Figure 5H
p_shap <- colMeans(abs(shap_contrib)) %>%
set_names(c("A+", "A-", "B+", "B-", "max(A)", "max(B)", "BIAS")) %>%
enframe() %>%
arrange(value) %>%
filter(name != "BIAS") %>%
ggplot(aes(x=reorder(name, value), y=value)) + geom_col() + coord_flip() + ylab("Mean Shapley Value") + xlab("Feature")
p_shap
p_comb_score <- tibble(pred = feats_mat_comb_df$pred, y = feats_mat_comb_df$dAB) %>%
mutate(col = densCols(., bandwidth=0.06,colramp=colorRampPalette(c("white","lightblue", "blue", "darkblue", "yellow", "gold","orange","red", "darkred" )))) %>%
ggplot(aes(x=pred, y=y, col=col)) +
geom_point(shape=19, size=point_size) +
scale_color_identity() +
coord_cartesian(xlim = c(-1.1, 0.6), ylim = c(-1.8, 1.2)) +
xlab("Combined model") +
ylab("Meth (3a-/-) - (3b-/-)") +
theme(aspect.ratio=1, panel.grid.major=element_blank(), panel.grid.minor=element_blank()) +
labs(subtitle = glue("R^2 = {cor}", cor = round(cor(feats_mat_comb_df$pred, feats_mat_comb_df$dAB)^2, digits=2))) +
theme(plot.subtitle = ggtext::element_markdown())
p_comb_score
## [1] 0.4487908df <- gextract.left_join("DNMT.ab_score_xgb_plus", intervals=feats_mat_comb_df, iterator=feats_mat_comb_df) %>% select(comb_score = pred, dinuc_score = DNMT.ab_score_xgb_plus, dAB) %>% as_tibble()df %>%
filter(abs(dAB) <= 0.05 ) %>%
ggplot(aes(x=comb_score, y=dinuc_score)) +
scattermore::geom_scattermore() +
geom_abline(linetype="dashed") +
theme(aspect.ratio=1)
p_comb_vs_dinuc <- df %>%
mutate(col = densCols(., bandwidth=0.06,colramp=colorRampPalette(c("white","lightblue", "blue", "darkblue", "yellow", "gold","orange","red", "darkred" )))) %>%
ggplot(aes(x=comb_score, y=dinuc_score, col=col)) +
geom_point(shape=19, size=point_size) +
scale_color_identity() +
coord_cartesian(xlim = c(-1, 1), ylim = c(-1, 1)) +
xlab("Combined model") +
ylab("Dinuc score") +
theme(aspect.ratio=1, panel.grid.major=element_blank(), panel.grid.minor=element_blank()) +
labs(subtitle = glue("R^2 = {cor}", cor = round(cor(df$comb_score, df$dinuc_score)^2, digits=2))) +
theme(plot.subtitle = ggtext::element_markdown())
p_comb_vs_dinuc
## [1] 0.8948897Extract sequence from model
flank_bp <- 5
seq_df_wide <- get_seq_df(feats_mat_comb_df %>% select(chrom, start, end), flank_bp = flank_bp) %>% seq_df_to_wide(flank_bp = flank_bp)options(repr.plot.width = 5, repr.plot.height = 6)
p <- coef_df_comb %>%
ggplot(aes(x=pos, y=dinuc, fill=coefficient)) +
geom_tile() +
scale_fill_gradient2(low = "darkblue", high = "darkred", mid = "white", midpoint = 0, na.value="white") +
theme_minimal() +
ylab("Dinucleotide") +
xlab("Position")
p
5.1.6 Figure 5I
options(repr.plot.width = 5, repr.plot.height = 6)
df <- coef_df_comb %>%
select(pos, dinuc, coef_comb = coefficient) %>%
left_join(coef_df_ab %>% rename(coef = coefficient)) %>%
replace_na(replace = list(coef = 0, coef_comb = 0)) %>%
mutate(label = paste0(pos, ",", dinuc))## Joining, by = c("pos", "dinuc")df_scale <- df %>%
mutate_at(vars(coef, coef_comb), function(x) x - mean(x) ) %>%
mutate_at(vars(coef, coef_comb), function(x) x / sd(x) )
p <- df %>%
ggplot(aes(x=coef, y=coef_comb, label=label)) +
geom_point() +
geom_abline(linetype="dashed") +
theme_bw() +
theme(aspect.ratio=1)
p_scale <- df_scale %>%
ggplot(aes(x=coef, y=coef_comb, label=label)) +
geom_point(size=0.01) +
geom_abline(linetype="dashed") +
xlab("Dinuc model") +
ylab("Combined model") +
theme(aspect.ratio=1)
cor.test(df$coef, df$coef_comb, method="spearman")## Warning in cor.test.default(df$coef, df$coef_comb, method = "spearman"): Cannot
## compute exact p-value with ties##
## Spearman's rank correlation rho
##
## data: df$coef and df$coef_comb
## S = 38921, p-value < 2.2e-16
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.9217878
## Warning: ggrepel: 131 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
5.1.7 Figure 5J
epi6 <- fread(here("output/meissner_epi_cpg_meth_sum.tsv")) %>%
mutate(dAB_epi6 = ko3a - ko3b, dA_epi6 = ko3a - wt, dB_epi6 = ko3b - wt) %>%
filter(!is.na(dAB_epi6)) %>%
select(chrom, start, end, wt, ko3a, ko3b, dAB_epi6, dA_epi6, dB_epi6) %>%
as_tibble()
nrow(epi6)## [1] 13388epi6_scores <- gextract.left_join(c("DNMT.ab_score_comb", "DNMT.ab_score_xgb_plus"), intervals=epi6, iterator=epi6, colnames=c("comb_model", "eb_model")) %>%
select(-(chrom1:end1)) %>%
as_tibble()p_epi6_score <- epi6_scores %>%
select(eb_model, dAB_epi6) %>%
mutate(col = densCols(., bandwidth=bandwidth,colramp=colorRampPalette(c("white","lightblue", "blue", "darkblue", "yellow", "gold","orange","red", "darkred" )))) %>%
ggplot(aes(x=eb_model, y=dAB_epi6, col=col)) +
geom_point(shape=19, size=point_size) +
scale_color_identity() +
coord_cartesian(xlim = c(-1, 0.6), ylim = c(-0.4, 0.6)) +
xlab("EB model") +
ylab("Epi (3a-/-) – (3b-/-)") +
theme(aspect.ratio=1, panel.grid.major=element_blank(), panel.grid.minor=element_blank()) +
labs(subtitle = glue("r = {cor}", cor = round(cor(epi6_scores$dAB_epi6, epi6_scores$eb_model), digits=2)))
cor(epi6_scores$dAB_epi6, epi6_scores$eb_model)## [1] 0.6284416
p_epi6_score_comb <- epi6_scores %>%
select(comb_model, dAB_epi6) %>%
mutate(col = densCols(., bandwidth=bandwidth,colramp=colorRampPalette(c("white","lightblue", "blue", "darkblue", "yellow", "gold","orange","red", "darkred" )))) %>%
ggplot(aes(x=comb_model, y=dAB_epi6, col=col)) +
geom_point(shape=19, size=point_size) +
scale_color_identity() +
# coord_cartesian(xlim = c(-1, 0.6), ylim = c(-0.4, 0.6)) +
xlab("Comb model") +
ylab("Epi (3a-/-) – (3b-/-)") +
theme(aspect.ratio=1, panel.grid.major=element_blank(), panel.grid.minor=element_blank()) +
labs(subtitle = glue("r = {cor}", cor = round(cor(epi6_scores$dAB_epi6, epi6_scores$comb_model), digits=2)))
cor(epi6_scores$dAB_epi6, epi6_scores$comb_model)## [1] 0.6040253