def compute(self, X, y):
# turn data into a row vector (needed for libsvm)
X = asRowMatrix(X)
y = np.asarray(y)
self.svm.fit(X, y)
self.y = y
print(self.__repr__())
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 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):
XR = asRowMatrix(X);
y = np.asarray(y)
self._mean = {};
self._cov = {};
self._trans = {};
self._const = {};
unique_labels = np.unique(y);
for label in unique_labels:
groupX = XR[y == label, :];
self._mean[label] = np.squeeze(np.asarray(groupX.mean(axis=0)));
self._cov[label] = np.zeros(self._mean[label].shape);
_cov = np.cov(groupX, rowvar=0);
self._cov[label], self._trans[label] = np.linalg.eig(_cov);
self._cov[label] = np.asarray(self._cov[label].real, dtype=np.float64);
self._cov[label] = np.abs(self._cov[label]);
self._cov[label][self._cov[label] < self.EPS] = self.EPS;
self._trans[label] = np.asarray(self._trans[label].real, dtype=np.float64);
self._const[label] = np.log(self._cov[label]).sum();
def compute(self, X, y):
X = asRowMatrix(X)
y = np.asarray(y)
self.svm.fit(X, y)
self.y = y
def compute(self, X, y):
X = asRowMatrix(X)
y = np.asarray(y)
self.svm.fit(X, y)
self.y = y
