pavlov99

model = xgb.Booster(model_file='your.model')
model.feature_names = xgtrain.feature_names  # Note: xgtrain is your train file with features.
model.feature_types = xgtrain.feature_types

# Number of trees in the model
num_trees = len(model.get_dump())

# dump all of the trees to tree folder
for tree_index in range(num_trees):
    dot = xgb.to_graphviz(model, num_trees=tree_index)

karpathy

"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

snowman-repos

// Word Wrapping
@mixin word-wrap() {
  -ms-word-break: break-all;
  word-break: break-all;
  word-break: break-word;
  -webkit-hyphens: auto;
  -moz-hyphens: auto;
  hyphens: auto;
}
.word-wrap {