Killavus

class PrepareCalculationForB2BManager
  def prepare(quotation)
    CalculationForB2BManager.new(
      quotation.yearly_premium_value,
      quotation.yearly_offer_number,
      quotation.yearly_premium_url,
      quotation.position_in_leads,
      quotation.token,
      quotation.save_quote_key,
      quotation.competitors

Killavus

def iteratorAtomsDepleted(iteratorAtoms):
  return all(map(lambda iteratorAtom: iteratorAtom[1], iteratorAtoms))

def combine(*args):
  listIterators = [[iter(passedList), False]  for passedList in args]
  result = []
  while (not iteratorAtomsDepleted(listIterators)):
    for index, iteratorAtom in enumerate(listIterators):
      try:
        iterator, alreadyDone = iteratorAtom

Killavus

# M1 (N x A) -> M2 (N x B) -> DM (A x B)
def ComputeDistanceMatrix(M1, M2):
  # For each r-th row: Sum(k = 1, N) M1(r, k) ** 2 
  SM1 = np.sum(M1 ** 2)

  # For each r-th row: Sum(k = 1, N) M2(r, k) ** 2
  SM2 = np.sum(M2 ** 2)

  # The last term: 2 * (M1 x M2^T)
  M1M2_2 = 2 * np.dot(M1, M2.T)

Killavus

const loginUnsuccessful = this.state.alert === "User Not Found";

classNames("alert", "alert-dismissable", {
  "alert-danger": loginUnsuccessful
  "alert-success": !loginUnsuccessful
});

Killavus

from collections import Counter  
  
for label in set(group_labels):
  train_mask = group_labels == label
  real_labels = train_labels[train_mask]
  pos_stat = Counter(real_labels).values()
  # pos_stat is info about quantity of each label - as a list of quantities

Killavus

For label 0: 0.867669753086
For label 1: 0.82792603512
For label 2: 0.878846730532
For label 3: 0.795057662006
For label 4: 0.886658781769
For label 5: 0.882785618599
For label 6: 0.840796804795
For label 7: 0.864856124316
For label 8: 0.869239583546
For label 9: 0.877142283582

Killavus

For label 0: 0.865175145618
Representative:  [ 0.]
======
For label 1: 0.869355945887
Representative:  [ 6.]
======
For label 2: 0.828103005511
Representative:  [ 0.]
======
For label 3: 0.886468632048

Killavus

from fuel.datasets.cifar10 import CIFAR10
from sklearn.cluster import KMeans
from collections import Counter
from scipy.stats.mstats import mode

import numpy as np

def PerformKMeansAnalysis(train_data, train_labels, test_data, test_labels):
    naive_grouping = KMeans(n_clusters=10, n_init=5, n_jobs=8)
    naive_grouping.fit(train_data)

Killavus

svc = SGDClassifier(loss='squared_hinge', verbose=1, n_jobs=8, n_iter=100)
svc.fit(train_data, train_labels)
predictions = svc.predict(validation_data)
wrong = np.nonzero(validation_labels - predictions)[0].astype(np.float).shape[0]
print (float(wrong) / predictions.shape[0])

Killavus

    view.instance().populateSubnavigation(secondaryURLMapOne);

    const secondaryNav = view.find(".navbar-rt-secondary");
    const navItems = secondaryNav.find(NavItem);

    assert.equal(1, navItems.length);