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import random | |
class Markov(object): | |
def __init__(self, open_file): | |
self.cache = {} | |
self.open_file = open_file | |
self.words = self.file_to_words() | |
self.word_size = len(self.words) | |
self.database() |
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""" | |
Three ways of computing the Hellinger distance between two discrete | |
probability distributions using NumPy and SciPy. | |
""" | |
import numpy as np | |
from scipy.linalg import norm | |
from scipy.spatial.distance import euclidean | |
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from __future__ import division | |
import numpy as np | |
import pandas as pd | |
import random | |
def sample(data): | |
sample = [random.choice(data) for _ in xrange(len(data))] | |
return sample | |
def bootstrap_t_test(treatment, control, nboot = 1000, direction = "less"): |
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from pyspark import SparkContext | |
import numpy as np | |
from sklearn.cross_validation import train_test_split, Bootstrap | |
from sklearn.datasets import make_classification | |
from sklearn.metrics import accuracy_score | |
from sklearn.tree import DecisionTreeClassifier | |
def run(sc): |
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""" | |
(C) Mathieu Blondel - 2010 | |
License: BSD 3 clause | |
Implementation of the collapsed Gibbs sampler for | |
Latent Dirichlet Allocation, as described in | |
Finding scientifc topics (Griffiths and Steyvers) | |
""" |
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def better_table(table, caption, name): | |
start = r""" | |
\begin{{table}}[!htb] | |
\sisetup{{round-mode=places, round-precision=2}} | |
\caption{{{}}}\label{{table:{}}} | |
\centering | |
""".format(caption, name) | |
end = r"\end{table}" |
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