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Simple graphing of Elasticsearch scoring functions.
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from __future__ import division | |
import matplotlib.pyplot as plt | |
import matplotlib.dates as mdates | |
from pylab import * | |
import math | |
from scipy.stats import beta, norm, uniform | |
from scipy.special import betaln | |
from random import random, normalvariate | |
import numpy as np | |
from datetime import * | |
import os | |
import time | |
def graph(x, y, title): | |
plt.plot(x, y) | |
plt.suptitle( title ) | |
plt.show() | |
def gauss( origin, offset, scale, decay, abs_max, x_range, title ): | |
sigma = - scale**2 / ( 2 * math.log( decay ) ) | |
x = np.array(x_range) | |
y = np.exp(-(np.maximum(0, np.absolute(x-origin) - offset)**2)/2/sigma) | |
if ( abs_max > 0 ): | |
y = np.minimum( abs_max, y ) | |
graph( x, y, title ) | |
return y | |
def exp( origin, offset, scale, decay, abs_max, x_range, title ): | |
l = math.log( decay ) / scale | |
x = np.array(x_range) | |
y = np.exp(l * np.maximum(0, np.absolute(x-origin) - offset)) | |
if ( abs_max > 0 ): | |
y = np.minimum( abs_max, y ) | |
graph( x, y, title ) | |
return y | |
def log2p( mult, abs_max, x_range, title ): | |
x = np.array(x_range) | |
y = mult * np.log( x + 2 ) | |
if ( abs_max > 0 ): | |
y = np.minimum( abs_max, y ) | |
graph( x, y, title ) | |
return y | |
def sqrt( mult, abs_max, x_range, title ): | |
x = np.array(x_range) | |
y = mult * np.sqrt( x ) | |
if ( abs_max > 0 ): | |
y = np.minimum( abs_max, y ) | |
graph( x, y, title ) | |
return y | |
################################# | |
# Equations | |
sqrt( 0.1, 0, np.arange( 0, 1000000, 10 ), 'sqrt(active_installs) ' ) | |
x = np.arange( 0, 1000000, 10 ) | |
y1 = log2p( 0.375, 0, x, 'log2p(active_installs)' ) | |
y2 = exp( 1000000, 0, 900000, 0.75, 0, x, 'exp(active_installs)' ) | |
graph( x, y1 * y2, 'combined log2p(active_installs) * exp(active_installs)' ) | |
log2p( 0.25, 0, x, 'active installs' ) |
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