gt3

"""
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)

gt3

scaler_features = StandardScaler().fit(build_df[build_df.columns.values[:-1]])
scaled_features = scaler_features.transform(build_df[build_df.columns.values[:-1]])

scaler_label = StandardScaler().fit(np.array(build_df[build_df.columns.values[-1]]).reshape(-1, 1))
scaled_label = scaler_label.transform(np.array(build_df[build_df.columns.values[-1]]).reshape(-1, 1))


### Split data using train proportion of 0.7
train_size = int(scaled_features[:, :-1].shape[0] * 0.7)

gt3

Usable mainly when reducer is more like assembler for your data store changes, and main logic is put into action. So you often need to dispatch multiple smaller action, but to keep it atomic, they have to be processed in a single reduce step. This is the solution. The bonus is, it works well with devTools also.

Sourcecode:

multipleActionEnhancer.js:

export function multipleActionsEnhanceReducer(reducer) {
	return (state, action, ...rest) => {
		if (action.actions && action.actions.type && action.actions instanceof Array) {
			state = action.actions.reduce(reducer, state);
		} else {

gt3

// Epochs
const epochs = [
    ['year', 31536000],
    ['month', 2592000],
    ['day', 86400],
    ['hour', 3600],
    ['minute', 60],
    ['second', 1]
];

gt3

.markdown-here-wrapper {
  font-size: 16px;
  line-height: 1.8em;
  letter-spacing: 0.1em;
}


pre, code {
  font-size: 14px;
  font-family: Roboto, 'Courier New', Consolas, Inconsolata, Courier, monospace;