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))
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))
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
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()
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..."
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))
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')
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))
def compute(self, X, y):
X = asRowMatrix(X)
y = np.asarray(y)
self.svm.fit(X, y)
self.y = y