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from random import uniform | ||
from random import randint | ||
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from numpy import array | ||
from numpy.linalg import pinv as pinv # pseudo inverse aka dagger | ||
from numpy import dot | ||
from numpy import eye | ||
from numpy import size | ||
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def data_interval(low_b,high_b,N=100): | ||
'returns a list of N values uniformly distributed between low boundary and high boundary' | ||
d = [] | ||
for i in range(N): | ||
d.append(uniform(low_b,high_b)) | ||
return d | ||
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def data(N = 10): | ||
'return N random points (x1,x2)' | ||
d = [] | ||
for i in range(N): | ||
x = uniform(-1,1) | ||
y = uniform(-1,1) | ||
d.append([x,y]) | ||
return d | ||
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def data_from_file(filepath): | ||
'from a filepath returns a dataset with the form [[x1,x2],y]' | ||
datafile = open(filepath, 'r') | ||
data = [] | ||
for line in datafile: | ||
split = line.split() | ||
x1 = float(split[0]) | ||
x2 = float(split[1]) | ||
y = float(split[2]) | ||
data.append([ [x1,x2],y ]) | ||
return data | ||
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def randomline(): | ||
'computes a random line and returns [a,b] : y = ax + b' | ||
x1 = uniform(-1,1) | ||
y1 = uniform(-1,1) | ||
x2 = uniform(-1,1) | ||
y2 = uniform(-1,1) | ||
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a = abs(x1-x2)/abs(y1-y2) | ||
b = y1 - a*x1 | ||
return [a,b] # a*x + b | ||
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def target_function(coords): | ||
'from a coordinate input [a,b] returns the function a*x + b' | ||
f = lambda x: coords[0]*x + coords[1] | ||
return f | ||
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def target_random_function(coords): | ||
''' | ||
description: from a coordinate (coords) with the format [a,b] generated a random function. | ||
- coord: a list of the form [a,b] | ||
- returns: the generated random function that takes as argument a list with the form [x,y] | ||
and returns 1 or -1 whether y is below the linear function defined by a*x + b or above. | ||
''' | ||
func = target_function(coords) | ||
def f(X): | ||
x = X[0] | ||
y = X[1] | ||
if func(x) < y: | ||
return 1.0 | ||
else: | ||
return -1.0 | ||
return f | ||
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def signex(x,compare_to = 0): | ||
'returns +1 or -1 by comparing (x) to (compare_to) param (by default = 0)' | ||
if x > compare_to: | ||
return +1. | ||
else: | ||
return -1. | ||
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def sign(x,compare_to = 0): | ||
'returns +1 or -1 by comparing (x) to (compare_to) param (by default = 0)' | ||
if x > compare_to: | ||
return +1. | ||
else: | ||
return -1. | ||
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def map_point(point,f): | ||
'maps a point (x1,x2) to a sign -+1 following function f ' | ||
x1 = point[0] | ||
y1 = point[1] | ||
y = f(x1) | ||
compare_to = y1 | ||
return sign(y,compare_to) | ||
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def map_point_fmultipleparams(point,f): | ||
y1 = point[1] | ||
y = f(point) | ||
compare_to = y1 | ||
return sign(y,compare_to) | ||
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def build_training_set(data, func): | ||
t_set = [] | ||
for i in range(len(data)): | ||
point = data[i] | ||
y = map_point(point,func) | ||
t_set.append([ [ 1.0, point[0],point[1] ] , y ]) | ||
return t_set | ||
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def build_training_set_fmultipleparams(data,func): | ||
t_set = [] | ||
for i in range(len(data)): | ||
point = data[i] | ||
y = map_point_fmultipleparams(point,func) | ||
t_set.append([ [ 1.0, point[0],point[1] ] , y ]) | ||
return t_set | ||
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def print_avg(name,vector): | ||
print 'Average %s: %s'%(name,sum(vector)/(len(vector)*1.0)) | ||
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def target_vector(t_set): | ||
'creates a numpy array (eg a Y matrix) from the training set' | ||
y = array([t[1] for t in t_set]) | ||
return y | ||
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def input_data_matrix(t_set): | ||
'creates a numpy array (eg a X matrix) from the training set' | ||
X = array([t[0] for t in t_set]) | ||
return X | ||
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def pseudo_inverse(X): | ||
'dagger of pseudo matrix used for linear regression' | ||
return pinv(X) | ||
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def linear_regression(N_points,t_set): | ||
'''Linear regresion algorithm | ||
from Y and X compute the dagger or pseudo matrix | ||
return the Xdagger.Y as the w vector | ||
default lambda is 1.0 | ||
''' | ||
y_vector = target_vector(t_set) | ||
X_matrix = input_data_matrix(t_set) | ||
X_pseudo_inverse = pseudo_inverse(X_matrix) | ||
return dot(dot(X_pseudo_inverse,X_matrix.T),y_vector),X_matrix,y_vector | ||
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def linear_regression_lda(N_points,t_set,lda): | ||
'''Linear regresion algorithm | ||
from Y and X compute the dagger or pseudo matrix | ||
return the Xdagger.Y as the w vector | ||
default lambda is 1.0 | ||
''' | ||
y_vector = target_vector(t_set) | ||
X_matrix = input_data_matrix(t_set) | ||
X_pseudo_inverse = pseudo_inverse(dot(X_matrix.T,X_matrix)+lda*eye(size(X_matrix,1))) | ||
return dot(dot(X_pseudo_inverse,X_matrix.T),y_vector),X_matrix,y_vector | ||
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shino@macpro-santiago.lan.401 |
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caltech.ml | ||
========== | ||
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Machine learning course from Caltech | ||
Machine learning course from Caltech | ||
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utils_data: | ||
------------------------------------------------- | ||
data_interval(low_b,high_b,N=100) | ||
rename with random_values(low_b,high_b,size) | ||
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data(N = 10) | ||
rename with random_points(size) | ||
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data_from_file(filepath) | ||
rename dataset_from_file | ||
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build_training_set(data, func) | ||
rename build_dataset | ||
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build_training_set_fmultipleparams(data,func) | ||
rename build_dataset_fmultiparams | ||
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target_vector(t_set) | ||
rename target_vector(dataset) | ||
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input_data_matrix(t_set) | ||
rename data_matrix(dataset) | ||
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utils_math: | ||
------------------------------------------------ | ||
randomline() | ||
rename random_line_coefs | ||
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target_function(coords) | ||
rename linear_function(coefs) | ||
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target_random_function(coords) | ||
rename random_function()... | ||
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signex(x,compare_to = 0) | ||
delete use sign with an alias | ||
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sign(x,compare_to = 0) | ||
map_point(point,f) | ||
rename get_sign(point,f) | ||
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map_point_fmultipleparams(point,f) | ||
get_sign_multiparam | ||
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pseudo_inverse(X) | ||
check if needed | ||
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linear_regression(N_points,t_set,lda = 1.0) | ||
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linear_regression_lda(N_points,t_set,lda) | ||
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utils_print: | ||
----------------------------------------------- | ||
print_avg(name,vector) |
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