-
Notifications
You must be signed in to change notification settings - Fork 0
/
classalgorithms.py
302 lines (239 loc) · 10.5 KB
/
classalgorithms.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
from __future__ import division # floating point division
import numpy as np
import utilities as utils
from random import randrange
def getaccuracy(ytest, predictions):
correct = 0
for i in range(len(ytest)):
if ytest[i] == predictions[i]:
correct += 1
return (correct/float(len(ytest))) * 100.0
def geterror(ytest, predictions):
return (100.0-getaccuracy(ytest, predictions))
class Classifier:
"""
Generic classifier interface; returns random classification
Assumes y in {0,1}, rather than {-1, 1}
"""
def __init__( self, parameters={} ):
""" Params can contain any useful parameters for the algorithm """
self.params = {}
def reset(self, parameters):
""" Reset learner """
self.resetparams(parameters)
def resetparams(self, parameters):
""" Can pass parameters to reset with new parameters """
try:
utils.update_dictionary_items(self.params,parameters)
except AttributeError:
# Variable self.params does not exist, so not updated
# Create an empty set of params for future reference
self.params = {}
def getparams(self):
return self.params
def learn(self, Xtrain, ytrain):
""" Learns using the traindata """
def predict(self, Xtest):
probs = np.random.rand(Xtest.shape[0])
ytest = utils.threshold_probs(probs)
return ytest
class LinearRegressionClass(Classifier):
"""
Linear Regression with ridge regularization
Simply solves (X.T X/t + lambda eye)^{-1} X.T y/t
"""
def __init__( self, parameters={} ):
self.params = {'regwgt': 0.01}
self.reset(parameters)
def reset(self, parameters):
self.resetparams(parameters)
self.weights = None
def learn(self, Xtrain, ytrain):
""" Learns using the traindata """
# Ensure ytrain is {-1,1}
yt = np.copy(ytrain)
yt[yt == 0] = -1
# Dividing by numsamples before adding ridge regularization
# for additional stability; this also makes the
# regularization parameter not dependent on numsamples
# if want regularization disappear with more samples, must pass
# such a regularization parameter lambda/t
numsamples = Xtrain.shape[0]
self.weights = np.dot(np.dot(np.linalg.pinv(np.add(np.dot(Xtrain.T,Xtrain)/numsamples,self.params['regwgt']*np.identity(Xtrain.shape[1]))), Xtrain.T),yt)/numsamples
def predict(self, Xtest):
ytest = np.dot(Xtest, self.weights)
ytest[ytest > 0] = 1
ytest[ytest < 0] = 0
return ytest
class NaiveBayes(Classifier):
""" Gaussian naive Bayes; """
def __init__( self, parameters={} ):
""" Params can contain any useful parameters for the algorithm """
# Assumes that a bias unit has been added to feature vector as the last feature
# If usecolumnones is False, it ignores this last feature
self.params = {'usecolumnones': False}
self.reset(parameters)
def reset(self, parameters):
self.resetparams(parameters)
# TODO: set up required variables for learning
def learn(self, X, y):
if self.params['usecolumnones'] == False:
X = X[:,:-1]
self.para_c = [(X[y==ytype].mean(axis=0), X[y==ytype].var(axis=0)) for ytype in np.unique(y)]
self.prob_y = [sum(y==ytype)/len(y) for ytype in np.unique(y)]
def self_mult(self, lst):
prod = 1
for i in lst:
if np.isnan(i):
prod = prod*1
else:
prod = prod*i
return prod
def gauss(self, mu, var, x):
if var == 0:
if np.abs(x-mu) < 1e-2:
return 1.0
else:
return 0
else:
return ((1/np.sqrt(2*np.pi*var))*np.exp((x-mu)**2/(-2*var)))
def gauss_pdf(self, mu, var, x):
return ((1/np.sqrt(2*np.pi*var))*np.exp((x-mu)**2/(-2*var)))
def predict(self, X):
if self.params['usecolumnones'] == False:
X = X[:,:-1]
prob1 = np.array([self.self_mult(row) for row in self.gauss_pdf(self.para_c[0][0], self.para_c[0][1], X)])*self.prob_y[0]
prob0 = np.array([self.self_mult(row) for row in self.gauss_pdf(self.para_c[1][0], self.para_c[1][1], X)])*self.prob_y[1]
return np.array([0 if x>0 else 1 for x in prob1-prob0])
# TODO: implement learn and predict functions
class LogitReg(Classifier):
def __init__( self, parameters={} ):
# Default: no regularization
self.params = {'regwgt': 0.0, 'regularizer': 'None', 'lmbda1': .01, 'lmbda2': .1}
self.reset(parameters)
def reset(self, parameters):
self.resetparams(parameters)
self.weights = None
if self.params['regularizer'] is 'l1':
self.regularizer = 'l1'
self.regwgt = self.params['regwgt']
elif self.params['regularizer'] is 'l2':
self.regularizer = 'l2'
#print(self.params['regwgt'])
self.regwgt = self.params['regwgt']
elif self.params['regularizer'] is 'elasticNet':
self.regularizer = 'elasticNet'
self.lmbda1 = .01
self.lmbda2 = .1
else:
self.regularizer = 'basic'
def prox_func(self, delE, lmd):
return np.array([(x-lmd) if x>lmd else x+lmd if x<-lmd else 0 for x in delE])
def learn(self, X,y):
w = np.array([randrange(-10,10) for i in range(0,X.shape[1])])
epoch_error = []
alpha = .1
#self.regwgt = self.params['regwgt']
for epoch in range(0,500):
small_p = utils.sigmoid(np.dot(X,w))
err_old = geterror([1 if x>.5 else 0 for x in utils.sigmoid(np.dot(X,w))],y)
if self.regularizer == 'basic':
w = w - alpha*(X.T.dot(np.subtract(small_p,y)))
elif self.regularizer == 'l2':
w = w - alpha*(X.T.dot(np.subtract(small_p,y))+ 2*self.regwgt*w)
elif self.regularizer == 'l1':
w = self.prox_func(w - alpha*(X.T.dot(np.subtract(small_p,y))), self.regwgt)
elif self.regularizer == 'elasticNet':
w = self.prox_func(w - alpha*(X.T.dot(np.subtract(small_p,y)) + ((self.lmbda1*(1-self.lmbda2)))*w), self.lmbda1*self.lmbda2)
err_new = geterror([1 if x>.5 else 0 for x in utils.sigmoid(np.dot(X,w))],y)
if err_new>err_old:
alpha = alpha*.1
epoch_error.append(err_new)
self.weights = w
def predict(self, X):
return np.array([1 if x>.5 else 0 for x in utils.sigmoid(np.dot(X,self.weights))])
# TODO: implement learn and predict functions
class NeuralNet(Classifier):
def __init__(self, parameters={}):
self.params = {'nh': 4,
'transfer': 'sigmoid',
'stepsize': 0.01,
'epochs': 10}
self.reset(parameters)
def sigmoid(self, xvec):
return 1.0 / (1.0 + np.exp(np.negative(xvec)))
def reset(self, parameters):
self.resetparams(parameters)
if self.params['transfer'] is 'sigmoid':
self.transfer = utils.sigmoid
self.dtransfer = utils.dsigmoid
else:
# For now, only allowing sigmoid transfer
raise Exception('NeuralNet -> can only handle sigmoid transfer, must set option transfer to string sigmoid')
self.wi = None
self.wo = None
def learn(self, X, y):
hls = self.params['nh']
w2 = np.zeros(shape=(X.shape[1], hls))
w1 = np.array([randrange(-100,100) for i in range(0,hls)])
alpha = .001
for epoch in range(0,self.params['epochs']):
state = np.random.get_state()
np.random.shuffle(X)
np.random.set_state(state)
np.random.shuffle(y)
for t in range(0, np.shape(X)[0]):
y2 = utils.sigmoid(np.dot(X[t],w2))
y1 = self.sigmoid(np.dot(y2,w1.T))
del1 = y1-y[t]
grad1 = np.dot(del1.T,y2)
del2 = np.array([(w1*del1)[i]*y2[i]*(1-y2[i]) for i in range(len(w1))])
grad2 = np.array([X[t]*i for i in del2]).T
w2 = w2-alpha*grad2
w1 = w1-alpha*grad1
self.wi = w2
self.wo = w1
# TODO: implement learn and predict functions
def predict(self, X):
y2 = utils.sigmoid(np.dot(X,self.wi))
y1 = utils.sigmoid(np.dot(y2,self.wo.T))
return np.array([1 if x>.5 else 0 for x in y1])
def _evaluate(self, inputs):
"""
Returns the output of the current neural network for the given input
The underscore indicates that this is a private function to the class NeuralNet
"""
if inputs.shape[0] != self.ni:
raise ValueError('NeuralNet:evaluate -> Wrong number of inputs')
# hidden activations
ah = self.transfer(np.dot(self.wi,inputs))
# output activations
ao = self.transfer(np.dot(self.wo,ah))
return (ah, ao)
class LogitRegAlternative(Classifier):
def __init__( self, parameters={} ):
self.reset(parameters)
def reset(self, parameters):
self.resetparams(parameters)
self.weights = None
def pred(self, x):
a = np.sqrt(1+x**2)
return .5*(1+(x/a))
def gradient(self, x,y,w):
a = np.sqrt(1+np.dot(x,w)**2)
b = (x/a)*(y-self.pred(np.dot(x,w)))
return 2*b
def learn(self, X, y):
w = np.array([randrange(-10,10) for i in range(0,X.shape[1])])
alpha = .1
for epoch in range(0,100):
err_old = geterror([1 if x>.5 else 0 for x in self.pred(np.dot(X,w))],y)
grad = sum([self.gradient(X[i],y[i],w) for i in range(y.shape[0])])
w = w + alpha*grad
err_new = geterror([1 if x>.5 else 0 for x in self.pred(np.dot(X,w))],y)
if err_new>err_old:
alpha = alpha*.1
self.weights = w
def predict(self, X):
return np.array([1 if x>.5 else 0 for x in self.pred(np.dot(X,self.weights))])
# TODO: implement learn and predict functions