columns = np.hsplit(dataset,9) xsample = np.hstack(columns[0:8]) ysample = columns[8] shape = xsample.shape print "xsample = ",xsample.shape print "ysample = ",ysample.shape # indexList = np.random.permutation(shape[0]) indexList = range(shape[0]) x_train = xsample[indexList[0:538]] y_train = ysample[indexList[0:538]] print "x_train.shape = ",x_train.shape print "y_train.shape = ",y_train.shape x_test = xsample[indexList[538:]] y_test = ysample[indexList[538:]] print "x_test.shape = ",x_test.shape print "y_test.shape = ",y_test.shape classifier = BayesClassifier() classifier.saveNeeded = False classifier.saveNeeded = 20 classifier.train(x_train,y_train) print "classifier train succefully ..." y_predict = classifier.predict(x_test) result = classifier.f_measure(y_predict,y_test) print "BayesClassifier result = ",result
xsample = np.float32(xsample)
ysample = np.float32(ysample)
shape = xsample.shape
print "xsample = ", xsample.shape
print "ysample = ", ysample.shape
orders = ["precision", 'recall', 'accuracy', 'fmeasure']
number = 20
PRAFarray = np.zeros((number, 12))
for i in xrange(number):
print "i = ", i
indexList = np.random.permutation(shape[0])
x_train = xsample[indexList[:538]]
y_train = ysample[indexList[:538]]
classifier = BayesClassifier()
classifier.saveNeeded = False
classifier.sectionNumber = 30
classifier.train(x_train, y_train)
x_train1 = classifier.transform_pmodel(x_train)
x_train2 = np.hstack([x_train, x_train1])
print "shape = ", (x_train.shape, x_train1.shape, x_train2.shape)
x_test = xsample[indexList[538:]]
y_test = ysample[indexList[538:]]
x_test1 = classifier.transform_pmodel(x_test)
x_test2 = np.hstack([x_test, x_test1])
svm_params = dict(kernel_type=cv2.SVM_LINEAR,
svm_type=cv2.SVM_C_SVC,
C=3.67,
print "ysample = ",ysample.shape
# indexList = np.random.permutation(shape[0])
indexList = range(shape[0])
x_train = xsample[indexList[0:538]]
y_train = ysample[indexList[0:538]]
print "x_train.shape = ",x_train.shape
print "y_train.shape = ",y_train.shape
x_test = xsample[indexList[538:]]
y_test = ysample[indexList[538:]]
print "x_test.shape = ",x_test.shape
print "y_test.shape = ",y_test.shape
myBayes = BayesClassifier()
layers = np.array([8,15,1])
model = cv2.ANN_MLP()
model.create(layers)
params = dict( term_crit = (cv2.TERM_CRITERIA_COUNT, 3000, 0.01),
train_method = cv2.ANN_MLP_TRAIN_PARAMS_BACKPROP,
bp_dw_scale = 0.001,
bp_moment_scale = 0.0 )
model.train(x_train,y_train,None,params = params)
ret,resp = model.predict(x_test)
print "ysample = ", ysample.shape # indexList = np.random.permutation(shape[0]) indexList = range(shape[0]) x_train = xsample[indexList[0:538]] y_train = ysample[indexList[0:538]] print "x_train.shape = ", x_train.shape print "y_train.shape = ", y_train.shape x_test = xsample[indexList[538:]] y_test = ysample[indexList[538:]] print "x_test.shape = ", x_test.shape print "y_test.shape = ", y_test.shape classifier = BayesClassifier() classifier.saveNeeded = False classifier.sectionNumber = 32 classifier.train(x_train, y_train) print "classifier train succefully ..." y_predict = classifier.predict(x_test) # print "y_predict = ",y_predict print "y_predict = ", y_predict.shape accuracy = (y_test == y_predict) print "BayesClassifier accuracy = ", np.mean(accuracy) print "-" * 100 from sklearn.naive_bayes import BernoulliNB clf = BernoulliNB()
#!usr/bin/env/python
# -*- coding: utf-8 -*-
import numpy as np
from bayesClassifier import BayesClassifier
classifier = BayesClassifier()
classifier.sectionNumber = 12
classifier.saveNeeded = False
dataset = np.load('pima-indians.npy')
columns = np.hsplit(dataset, 9)
xsample = np.hstack(columns[0:8])
ysample = columns[8]
shape = xsample.shape
print "xsample = ", xsample.shape
print "ysample = ", ysample.shape
ysample = np.float32(ysample)
indexList = np.random.permutation(shape[0])
# indexList = range(shape[0])
x_train = xsample[indexList[0:538]]
y_train = ysample[indexList[0:538]]
print "x_train.shape = ", x_train.shape
print "y_train.shape = ", y_train.shape
classifier.train(x_train, y_train)
print "classifier train succefully ..."
x_train = classifier.transform_pmodel(x_train)
x_test = xsample[indexList[538:]]
#!usr/bin/env/python
# -*- coding: utf-8 -*-
import numpy as np
from bayesClassifier import BayesClassifier
classifier = BayesClassifier()
classifier.sectionNumber = 12
classifier.saveNeeded = False
dataset = np.load('pima-indians.npy')
columns = np.hsplit(dataset,9)
xsample = np.hstack(columns[0:8])
ysample = columns[8]
shape = xsample.shape
print "xsample = ",xsample.shape
print "ysample = ",ysample.shape
ysample = np.float32(ysample)
indexList = np.random.permutation(shape[0])
# indexList = range(shape[0])
x_train = xsample[indexList[0:538]]
y_train = ysample[indexList[0:538]]
print "x_train.shape = ",x_train.shape
print "y_train.shape = ",y_train.shape
classifier.train(x_train,y_train)
print "classifier train succefully ..."
x_train = classifier.transform_pmodel(x_train)
x_test = xsample[indexList[538:]]
# indexList = np.random.permutation(shape[0]) indexList = range(shape[0]) x_train = xsample[indexList[0:538]] y_train = ysample[indexList[0:538]] print "x_train.shape = ",x_train.shape print "y_train.shape = ",y_train.shape x_test = xsample[indexList[538:]] y_test = ysample[indexList[538:]] print "x_test.shape = ",x_test.shape print "y_test.shape = ",y_test.shape classifier = BayesClassifier() classifier.saveNeeded = False classifier.sectionNumber = 32 classifier.train(x_train,y_train) print "classifier train succefully ..." y_predict = classifier.predict(x_test) # print "y_predict = ",y_predict print "y_predict = ",y_predict.shape accuracy = (y_test == y_predict) print "BayesClassifier accuracy = ",np.mean(accuracy) print "-"*100
xsample = np.hstack(columns[0:8])
ysample = columns[8]
shape = xsample.shape
print "xsample = ", xsample.shape
print "ysample = ", ysample.shape
from sklearn.naive_bayes import GaussianNB
orders = ["precision", 'recall', 'accuracy', 'fmeasure']
number = 20
PRAFarray = np.zeros((number, 12))
for i in xrange(number):
indexList = np.random.permutation(shape[0])
x_train = xsample[indexList[:538]]
y_train = ysample[indexList[:538]]
classifier = BayesClassifier()
classifier.saveNeeded = False
classifier.sectionNumber = 40
classifier.train(x_train, y_train)
x_train1 = classifier.transform_pmodel(x_train)
x_train2 = np.hstack([x_train, x_train1])
print "shape = ", (x_train.shape, x_train1.shape, x_train2.shape)
x_test = xsample[indexList[538:]]
y_test = ysample[indexList[538:]]
x_test1 = classifier.transform_pmodel(x_test)
x_test2 = np.hstack([x_test, x_test1])
clf = GaussianNB().fit(x_train, y_train.ravel())
y_predict = clf.predict(x_test)
result = classifier.f_measure(y_predict, y_test)
indexList = np.random.permutation(shape[0]) # indexList = range(shape[0]) x_train = xsample[indexList[0:538]] y_train = ysample[indexList[0:538]] print "x_train.shape = ",x_train.shape print "y_train.shape = ",y_train.shape x_test = xsample[indexList[538:]] y_test = ysample[indexList[538:]] print "x_test.shape = ",x_test.shape print "y_test.shape = ",y_test.shape classifier = BayesClassifier() classifier.saveNeeded = True classifier.sectionNumber = 12 classifier.train(x_train,y_train) print "classifier train succefully ..." y_predict = classifier.predict(x_test) # print "y_predict = ",y_predict print "y_predict = ",y_predict.shape accuracy = (y_test == y_predict) print "BayesClassifier accuracy = ",np.mean(accuracy) params = classifier.params ''' p = params['p0'] p0 = p['class0']
ysample = np.float32(ysample)
shape = xsample.shape
print "xsample = ",xsample.shape
print "ysample = ",ysample.shape
orders = ["precision",'recall','accuracy','fmeasure']
number = 20
PRAFarray = np.zeros((number,12))
for i in xrange(number):
print "i = ",i
indexList = np.random.permutation(shape[0])
x_train = xsample[indexList[:538]]
y_train = ysample[indexList[:538]]
classifier = BayesClassifier()
classifier.saveNeeded = False
classifier.sectionNumber = 39
classifier.train(x_train,y_train)
x_train1 = classifier.transform_pmodel(x_train)
x_train2 = np.hstack([x_train,x_train1])
# print "shape = ",(x_train.shape,x_train1.shape,x_train2.shape)
x_test = xsample[indexList[538:]]
y_test = ysample[indexList[538:]]
x_test1 = classifier.transform_pmodel(x_test)
x_test2 = np.hstack([x_test,x_test1])
bdt = AdaBoostClassifier(DecisionTreeClassifier(max_depth=1),
algorithm="SAMME",
print "ysample = ", ysample.shape
# indexList = np.random.permutation(shape[0])
indexList = range(shape[0])
x_train = xsample[indexList[0:538]]
y_train = ysample[indexList[0:538]]
print "x_train.shape = ", x_train.shape
print "y_train.shape = ", y_train.shape
x_test = xsample[indexList[538:]]
y_test = ysample[indexList[538:]]
print "x_test.shape = ", x_test.shape
print "y_test.shape = ", y_test.shape
myBayes = BayesClassifier()
layers = np.array([8, 15, 1])
model = cv2.ANN_MLP()
model.create(layers)
params = dict(term_crit=(cv2.TERM_CRITERIA_COUNT, 3000, 0.01),
train_method=cv2.ANN_MLP_TRAIN_PARAMS_BACKPROP,
bp_dw_scale=0.001,
bp_moment_scale=0.0)
model.train(x_train, y_train, None, params=params)
ret, resp = model.predict(x_test)