def predict(self, X):
'''
Returns +1 if the sample is predicted to be novel, -1 otherwise.
'''
ks = metrics.pairwise_kernels(X=self.X_train, Y=X, metric=self.metric)
scores = score(self.projection, self.target_points, ks)
prediction = np.array([1 if sc > self.threshold else -1 for sc in scores])
return prediction
def predict(self, X):
'''
Returns +1 if the sample is predicted to be novel, -1 otherwise.
'''
ks = metrics.pairwise_kernels(X=self.X_train, Y=X, metric=self.metric)
scores = score(self.projection, self.target_points, ks)
prediction = np.array(
[1 if sc > self.threshold else -1 for sc in scores])
return prediction
def predict(self, X):
'''
Returns +1 if the sample is predicted to be novel, -1 otherwise.
The threshold is selected between 0 and the minimum distance between
two target points.
'''
ks = metrics.pairwise_kernels(X=self.X_train, Y=X, metric=self.metric)
scores = score(self.projection, self.target_points, ks)
# min_dist = self._get_pairwise_min_dist()
prediction = np.array([1 if sc > self.threshold else -1 for sc in scores])
return scores
def predict(self, X):
'''
Returns +1 if the sample is predicted to be novel, -1 otherwise.
The threshold is selected between 0 and the minimum distance between
two target points.
'''
ks = metrics.pairwise_kernels(X=self.X_train, Y=X, metric=self.metric)
scores = score(self.projection, self.target_points, ks)
# min_dist = self._get_pairwise_min_dist()
prediction = np.array(
[1 if sc > self.threshold else -1 for sc in scores])
return scores
