sergeyf

'''
Based on: https://gist.github.com/bshillingford/6259986edca707ca58dd
Modified to work on Windows by: Sergey Feldman
Jan 17, 2016

Requirements: pdflatex, bibtex
'''

import requests
import lxml.html as html

sergeyf

import numpy as np
from sklearn.feature_extraction import image
from sklearn.cluster import MiniBatchKMeans
from sklearn import cross_validation, svm, datasets
from sklearn.datasets import fetch_olivetti_faces, fetch_mldata
from matplotlib import pylab as pl

def HIK_kernel(X,Y):
    return np.array([[np.sum(np.minimum(x,y)) for y in Y] for x in X]) 
    

sergeyf

from scipy.stats import rv_continuous
from scipy.special import gammaln, gammaincinv, gammainc
from numpy import log,exp

class igamma_gen(rv_continuous):
  def _pdf(self, x, a, b):
      return exp(self._logpdf(x,a,b))
  def _logpdf(self, x, a, b):
      return a*log(b) - gammaln(a) -(a+1)*log(x) - b/x
  def _cdf(self, x, a, b):

sergeyf

import numpy as np
import matplotlib.pyplot
mu, sigma = 3., 1. # mean and standard deviation
s = np.random.lognormal(mu, sigma, 10000)
log_s = np.log(s)

subplot(211)
count,bins,_ = hist(s, 100, normed=True, align='mid')
x = np.linspace(min(bins), max(bins), 10000)
pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2)) / (x * sigma * np.sqrt(2 * np.pi)))