Cameron Davidson-Pilon CamDavidsonPilon
CamDavidsonPilon
/ multiderivative.py
Last active
December 15, 2015 09:59
This is a recursive implementation of evaluating PDF of a Cumulative Density Function (CDF) at a point `u`, in arbitrary dimension, using finite difference methods.
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| def cdf2pdf( f, u, delta=0.001, kwargs={} ): | |
| """ | |
| Computes every partial derivative of the CDF to find the underlying PDF. | |
| f: a numpy vectorized python/numpy function | |
| u: the point to evaluate the CDF at. | |
| delta: the precision | |
| kwargs: additional keywords args to f | |
| """ |
CamDavidsonPilon
/ gist:5437569
Created
April 22, 2013 19:00
— forked from shanselman/gist:5422230
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| { | |
| {I have|I've} been {surfing|browsing} online more than {three|3|2|4} hours today, yet I never found any interesting article like yours. {It's|It | |
| is} pretty worth enough for me. {In my opinion|Personally|In my view}, if all {webmasters|site owners|website owners|web owners} and bloggers made good content as | |
| you did, the {internet|net|web} will be {much more|a lot more} | |
| useful than ever before.| | |
| I {couldn't|could not} {resist|refrain from} commenting. {Very well|Perfectly|Well|Exceptionally well} written!| | |
| {I will|I'll} {right away|immediately} {take hold of|grab|clutch|grasp|seize|snatch} | |
| your {rss|rss feed} as I {can not|can't} {in finding|find|to find} your {email|e-mail} subscription {link|hyperlink} or {newsletter|e-newsletter} service. Do {you have|you've} any? | |
| {Please|Kindly} {allow|permit|let} me {realize|recognize|understand|recognise|know} {so that|in order that} I {may just|may|could} subscribe. | |
| Thanks.| |
CamDavidsonPilon
/ snapple.py
Last active
December 16, 2015 12:58
using data from https://gist.github.com/shanselman/5422230 as `filename`, will create a random comment.
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| import re | |
| import random | |
| CURLY_RE = re.compile( "\{(.*?)\}" ) | |
| def spam( filename ): | |
| file = open(filename, "r") |
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| import numpy as np | |
| from sklearn.linear_model import LinearRegression | |
| #create some random data. | |
| x1 = np.random.randn(250,1) | |
| x2 = 0.001*np.random.randn(250,1) - x1 | |
| x3 = np.random.randn(250,1) | |
| # Y is a linear combination of the created data, with | |
| # weights (10,10,0.01) |
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| import android | |
| from time import sleep | |
| from requests import post | |
| droid = android.Android() | |
| SERVER = "http://artech.herokuapp.com" | |
| ID = 0 | |
| """ |
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| %pylab | |
| import scipy.stats as stats | |
| print "Best to use custom matplotlibrc located in /styles directory of BMH project." | |
| figure( figsize= ( 8, 11 ) ) | |
| #means = np.random.uniform( -4, 4, size = 5 ) | |
| means = np.array([ 3.90933614, -1.15148996, 1.70045292, -3.27613385, -0.71058847]) |
CamDavidsonPilon
/ biased_coin.py
Created
November 24, 2013 22:21
biased coins to unbiased (heh cheating)
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| import random | |
| p1 = 0.9 | |
| p2 = 0.1 | |
| N = 10000 | |
| s = 0.0 | |
| i = 0 | |
| while i < N: |
CamDavidsonPilon
/ findN.ipynb
Last active
January 1, 2016 11:39
find a bound on the expected number of samples needed for an A/B test.
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| # using %matploblib <backend> also imports the following helpers | |
| from matplotlib import pylplot as plt | |
| from IPython.core.pylabtools import figsize |
CamDavidsonPilon
/ exponential_survival_data.py
Last active
August 29, 2015 13:56
Generate exponential survival data with probability alpha of censorship
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| def exponential_survival_data(n, cr=0.05, scale=1.): | |
| t = stats.expon.rvs(scale=scale, size=n) | |
| if cr == 0.0: | |
| return t, np.ones(n, dtype=bool) | |
| def pF(h): | |
| v = 1.0*h/scale | |
| return v / (np.exp(v)-1) - cr |
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