nbsvm/nbsvm.py
def predict(self, X):
        "Perform classification on samples in X."
        X_nb = X*self.r
        preds = np.add(np.dot(X_nb, self.interpolated_coef_.transpose()), self.interpolated_intercept_)
        preds[preds>0] = 1
        preds[preds<=0] = 0 
        return preds
Perform classification on samples in X.
Perform classification on samples in X.
Perform classification on samples in X .
def predict(self, X):
        X_nb = X*self.r
        preds = np.add(np.dot(X_nb, self.interpolated_coef_.transpose()), self.interpolated_intercept_)
        preds[preds>0] = 1
        preds[preds<=0] = 0 
        return preds
def predict ( self , X ) : X_nb = X * self . r preds = np . add ( np . dot ( X_nb , self . interpolated_coef_ . transpose ( ) ) , self . interpolated_intercept_ ) preds [ preds > 0 ] = 1 preds [ preds <= 0 ] = 0 return preds
Perform classification on samples in X.
Perform classification on samples in X .
"Perform classification on samples in X."
self
X