def train(self):
if (self.status != 'init'):
print("Please load train data and init W first.")
return self.W
self.status = 'train'
self.svm_processor = svm.BinaryClassifier()
self.svm_processor.load_train_data()
self.svm_processor.train_X = self.train_X
self.svm_processor.train_Y = self.train_Y
self.svm_processor.set_param(svm_kernel=self.svm_kernel, gamma=self.gamma, C=self.C)
self.svm_processor.init_W()
self.svm_processor.train()
# slow
svm_transform_X = np.apply_along_axis(self.svm_score, axis=1, arr=self.train_X)
svm_transform_X = np.reshape(svm_transform_X, (-1, 1))
svm_transform_X0 = np.reshape(np.ones(self.data_num), (-1, 1))
svm_transform_X = np.concatenate((svm_transform_X0, svm_transform_X), axis=1)
self.logistic_processor = logistic_regression.LogisticRegression()
self.logistic_processor.load_train_data()
self.logistic_processor.train_X = svm_transform_X
self.logistic_processor.train_Y = self.train_Y
self.logistic_processor.set_param(feed_mode=self.feed_mode, step_eta=self.step_eta, updates=self.updates)
self.logistic_processor.init_W()
self.logistic_processor.train()
return self.W
# encoding=utf8
import os
import numpy as np
import FukuML.Utility as utility
import FukuML.SupportVectorMachine as svm
input_train_data_file = os.path.join(
os.path.join(os.getcwd(), os.path.dirname(__file__)),
'FukuML/dataset/emotion.dat')
cross_validator = utility.CrossValidator()
svm_mc = svm.MultiClassifier()
svm_mc.load_train_data(input_train_data_file)
svm_mc.set_param(svm_kernel='soft_gaussian_kernel', gamma=1, C=5)
cross_validator.add_model(svm_mc)
avg_errors = cross_validator.excute()
print(avg_errors)
svm_mc = svm.MultiClassifier()
svm_mc.load_train_data(input_train_data_file)
svm_mc.set_param(svm_kernel='soft_gaussian_kernel', gamma=1, C=5)
svm_mc.init_W()
svm_mc.train()
'''
for class_item in svm_mc.class_list:
print(class_item)
print(svm_mc.classifier_list[class_item].alpha)
error_sv = (svm_mc.classifier_list[class_item].alpha > 0.999999999)
print(np.arange(len(svm_mc.classifier_list[class_item].alpha))[error_sv])
#encoding=utf8
import os
import FukuML.Utility as utility
import FukuML.SupportVectorMachine as svm
input_train_data_file = os.path.join(
os.path.join(os.getcwd(), os.path.dirname(__file__)),
'library/iNDIEVOX-Dataset/dataset/emotion_combine_song_train.dataset')
cross_validator = utility.CrossValidator()
svm_mc1 = svm.MultiClassifier()
svm_mc1.load_train_data(input_train_data_file)
svm_mc1.set_param(svm_kernel='soft_gaussian_kernel', C=1)
svm_mc2 = svm.MultiClassifier()
svm_mc2.load_train_data(input_train_data_file)
svm_mc2.set_param(svm_kernel='soft_gaussian_kernel', C=10)
svm_mc3 = svm.MultiClassifier()
svm_mc3.load_train_data(input_train_data_file)
svm_mc3.set_param(svm_kernel='soft_gaussian_kernel', C=100)
print("\n10 fold cross validation：")
cross_validator.add_model(svm_mc1)
cross_validator.add_model(svm_mc2)
cross_validator.add_model(svm_mc3)
avg_errors = cross_validator.excute()
print("\n各模型驗證平均錯誤：")
print(avg_errors)