コード例 #1
0
ファイル: model.py プロジェクト: imclab/YCHack-MakeHAL9000
 def compute(self, X, y):
     [D, self.W, self.mu] = fisherfaces(asRowMatrix(X), y, self.num_components)
     # store labels
     self.y = y
     # store projections
     for xi in X:
         self.projections.append(project(self.W, xi.reshape(1, -1), self.mu))
コード例 #2
0
ファイル: model.py プロジェクト: oloa/Tinder-ML
	def compute(self, X, y):
		[D, self.W, self.mu] = fisherfaces(asRowMatrix(X),y, self.num_components)
		# store labels
		self.y = y
		# store projections
		for xi in X:
			self.projections.append(project(self.W, xi.reshape(1,-1), self.mu))
コード例 #3
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 def compute(self, X, y):
     self.logger.debug("SVM TRAINING (C=%.2f,gamma=%.2f,p=%.2f,nu=%.2f,coef=%.2f,degree=%.2f)" % (
     self.param.C, self.param.gamma, self.param.p, self.param.nu, self.param.coef0, self.param.degree))
     # turn data into a row vector (needed for libsvm)
     X = asRowMatrix(X)
     y = np.asarray(y)
     problem = svm_problem(y, X.tolist())
     self.svm = svm_train(problem, self.param)
     self.y = y
コード例 #4
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def grid_search(model, X, y, tuned_parameters):
    # Check if the Classifier in the Model is actually an SVM:
    if not isinstance(model.classifier, SVM):
        raise TypeError("classifier must be of type SVM!")
    # First compute the features for this SVM-based model:
    features = model.feature.compute(X, y)
    # Turn the List of Features into a matrix with each feature as Row:
    Xrow = asRowMatrix(features)
    # Split the dataset in two equal parts
    X_train, X_test, y_train, y_test = train_test_split(Xrow,
                                                        y,
                                                        test_size=0.5,
                                                        random_state=0)
    # Define the Classifier:
    scores = ['precision', 'recall']
    # Evaluate the Model:
    for score in scores:
        print("# Tuning hyper-parameters for %s" % score)
        print()

        clf = GridSearchCV(SVC(C=1),
                           tuned_parameters,
                           cv=5,
                           scoring='%s_macro' % score)
        clf.fit(X_train, y_train)

        print("Best parameters set found on development set:")
        print()
        print(clf.best_params_)
        print()
        print("Grid scores on development set:")
        print()
        means = clf.cv_results_['mean_test_score']
        stds = clf.cv_results_['std_test_score']
        for mean, std, params in zip(means, stds, clf.cv_results_['params']):
            print("%0.3f (+/-%0.03f) for %r" % (mean, std * 2, params))
        print()

        print("Detailed classification report:")
        print()
        print("The model is trained on the full development set.")
        print("The scores are computed on the full evaluation set.")
        print()
        y_true, y_pred = y_test, clf.predict(X_test)
        print(classification_report(y_true, y_pred))
        print()
コード例 #5
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    def compute(self, X, y):
        if not self.W and not self.mu:
            # 主成分分析,获取特征值,特征向量,和平均值
            [D, self.W, self.mu] = fisherfaces(asRowMatrix(X), y,
                                               self.num_components)
        print "特征值,特征向量,和平均值 计算完毕...."
        # store labels
        # 识别的类别存放的地方
        self.y = y
        self.X = X
        # store projections
        for xi in X:
            # 预处理
            # 将图像与特征向量做点积
            self.projections.append(project(self.W, xi.reshape(1, -1),
                                            self.mu))

        print "预处理完毕..."
コード例 #6
0
ファイル: models.py プロジェクト: tmurray19/CS428
 def compute(self, X, y):
     [D, self.W, self.mu] = pca(asRowMatrix(X), y, self.num_components)
     self.y = y
     for xi in X:
         self.projections.append(project(self.W, xi.reshape(1, -1),
                                         self.mu))
コード例 #7
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import sys
import numpy as np
from subspace import pca
from util import normalize, asRowMatrix, read_images
from visual import subplot

import matplotlib.cm as cm

[X, y] = read_images('./faces')
print(np.asarray(X).shape)
print(asRowMatrix(X).shape)
# [D, W, mu] = pca(asRowMatrix(X), y)

# E=[]
# for i in xrange(min(len(X), 16)):
# 	e = W[:, i].reshape(X[0].shape)
# 	E.append(normalize(e, 0, 255))

# subplot(title='Eigenface', images=E, rows=4, cols=4, sptitle='Eigenface', colormap=cm.jet, filename='python_pca_eigenfaces.png')
コード例 #8
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	def train(self, X, y):
		[D, self.W, self.mu] = PCA(asRowMatrix(X),y)
		self.y = y
		for xi in self.X:
			self.projections.append(project(self.W, xi.reshape(1,-1), self.mu))
コード例 #9
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 def compute(self, X, y):
     X = asRowMatrix(X)
     y = np.asarray(y)
     self.svm.fit(X, y)
     self.y = y