kocur4d

alias irbg='irb -I ./lib -r ./lib/*.rb'

kocur4d

git config --global alias.co checkout
git config --global alias.br branch
git config --global alias.ci commit
git config --global alias.st status

git config --global alias.ust 'reset HEAD --'

kocur4d

data = np.array([1,2,3,4,5])

np.pad(data, (2, 3), 'constant', constant_values=9)
# [9,9,1,2,3,4,5,9,9,9]

np.pad(data, (0, 3), 'constant', constant_values=9)
# [1,2,3,4,5,9,9,9]

np.pad(data, (2, 0), 'constant', constant_values=9)
# [9,9,1,2,3,4,5]

kocur4d

# count the number of expected result
sum(record.outcome == 1 for record in data.records)

# Count the values
df.column_name.value_counts()
# or
df.groupby('column_name').count()

kocur4d

yarn add redux react-redux redux-thunk redux-devtools-extension redux-logger immutable redux-immutable node-sass-chokidar

rm src/App* src/logo*

Create the store

// ./src/store.js

import { createStore, applyMiddleware, compose } from 'redux'

kocur4d

from sklearn.learning_curve import learning_curve
import numpy as np
import matplotlib.pyplot as plt

N, train_lc, val_lc = learning_curve(svc_clf, X_train, y_train, cv=7, train_sizes=np.linspace(0.3, 1, 25))

plt.plot(N, np.mean(train_lc, 1), color='blue', label='training score')
plt.plot(N, np.mean(val_lc, 1), color='red', label='validation score')
plt.hlines(np.mean([train_lc[-1], val_lc[-1]]), N[0], N[-1], color='gray', linestyle='dashed')
plt.set_ylim(0, 1)

kocur4d

// SAGA
export const disconnectPage = function*(pageId) {
  const accessToken = yield call(ensureAccessTokenSaga)

  const { success, data, error } = yield call(disconnectFacebookPage(pageId), accessToken)
  if(success === true) {
    yield put(dataRequest({
      key: 'facebook_page_settings:pages',
      fetcher: fetchFacebookPages,
    }))

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const randomColor = () => '#'+(Math.random()*0xFFFFFF<<0).toString(16)

kocur4d

import numpy as np

X = np.array([
    [2.31, 4.58],
    [1.56,4.27],
    [5.48,5.61],
    [0.03,1.49],
    [6.13,6.07],
    [2.36,4.75],
    [2.14,4.3],