chi-squared test
import scipy.stats
obs = np.array([[a, b],
[c, d]])
_, p, _, _ = scipy.stats.chi2_contingency(obs)chi-squared test
import scipy.stats
obs = np.array([[a, b],
[c, d]])
_, p, _, _ = scipy.stats.chi2_contingency(obs)| # inspired by and based on: | |
| # https://julien.danjou.info/python-exceptions-guide/ | |
| # https://github.com/jd/julien.danjou.info/blob/master/bin/generate-python-exceptions-graph.py | |
| import builtins | |
| edges = set() | |
| synonyms = {} | |
| for name in dir(builtins): | |
| item = getattr(builtins, name) |
| import random | |
| import math | |
| def mean(elements): | |
| return sum(elements) / len(elements) | |
| def euclidean_dist(first, second): | |
| assert len(first) == len(second) | |
| return sum((f - s)**2 for f, s in zip(first, second))**0.5 |
| import time | |
| import pygame | |
| import gym | |
| frame_time = 1.0 / 15 # seconds | |
| pygame.init() |
| import tensorflow as tf | |
| w = tf.Variable([2.0]) | |
| b = tf.Variable([2.0]) | |
| x = tf.constant([1.0]) | |
| y = tf.sigmoid(w*x + b) | |
| y_ = tf.constant([0.0]) | |
| cross_entropy = -1*(y_*tf.log(y) + (1-y_)*(tf.log(1-y))) | |
| s = tf.Session() | |
| s.run(tf.initialize_all_variables()) |
This is Bostock's interactive Reingold-Tilford Tree with data representing the rules of a simple sklearn decision tree. Click on nodes to expand or collapse.
For more, see the complete write-up.
| def _find_getch(): | |
| # courtesy of Louis | |
| # http://stackoverflow.com/questions/510357/ | |
| try: | |
| import termios | |
| except ImportError: | |
| # Non-POSIX. Return msvcrt's (Windows') getch. | |
| import msvcrt | |
| return msvcrt.getch |
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