Created
December 17, 2015 18:56
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Here are some kernels I like to use for Machine Learning related projects.
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#!/usr/bin/python | |
# -*- coding: utf-8 -*- | |
__author__ = ('Kaan Akşit') | |
__version__ = '0.1' | |
# Importing necessary libraries. | |
try: | |
import sys,glob,time,copy | |
import numpy as np | |
from numpy.linalg import pinv | |
from math import e | |
except ImportError, err: | |
print "couldn't load module. %s" % (err) | |
sys.exit() | |
# Cosine distance calculation in between two vectors. | |
def CosDis(a,b): | |
dividend = np.dot(a,b) | |
denominator = np.linalg.norm(a,2)*np.linalg.norm(b,2) | |
return dividend*1./denominator | |
# Minkowski distance calculation in between two vectors. | |
def MinDis(a,b,p): | |
return np.linalg.norm(a-b,p) | |
# Chessboard distance calculation in between two vectors. | |
def ChessDis(a,b): | |
return np.amax(abs(a-b)) | |
# Bray curtis distance calculation in between two vectors. | |
def BrayDis(a,b): | |
dividend = np.sum(abs(a-b)) | |
denominator = np.sum(abs(a+b)) | |
return dividend*1./denominator | |
# Canberra distance calculation in between two vectors. | |
def CanberraDis(a,b): | |
dividend = abs(a-b) | |
denominator = abs(a)+abs(b) | |
return np.sum(dividend*1./denominator) | |
# Manhattan distance calculation in between two vectors. | |
def ManhattanDis(a,b): | |
return np.sum(abs(a-b)) | |
# Those kernel are learned from http://crsouza.com/2010/03/kernel-functions-for-machine-learning-applications/ | |
# Radial basis calculation in between two vectors. | |
def RadialBasis(a,b,sigma=2.): | |
gamma = 1./2./sigma**2 | |
return np.exp(-gamma*np.sqrt(np.sum((a-b)**2))) | |
# Exponential kernel calculation in between two vectors. | |
def Exponential(a,b,sigma=2.): | |
gamma = 1./2./sigma**2 | |
return np.exp(-gamma*np.sqrt(np.sum((a-b)))) | |
# Laplacian kernel calculation in between two vectors. | |
def Laplacian(a,b,sigma=2.): | |
gamma = 1./sigma | |
return np.exp(-gamma*np.sqrt(np.sum((a-b)))) | |
# Linear kernel calculation in between two vectors. | |
def Linear(a,b,sigma=0.): | |
return np.dot(a,b.T)+sigma | |
# Bare call response. | |
if __name__ == '__main__': | |
pass | |
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