def predict(self, X): tmp_file = path.join(self.tmp_dir,'net.mat') results = matlab_command(("load(tmp_file);" "y_pred = vec2ind(net(X));"), X = X.T, tmp_file = tmp_file) return (results['y_pred'].ravel() - 1) #dammit matlab...
def confidence(self, X): tmp_file = path.join(self.tmp_dir,'net.mat') results = matlab_command(("load(tmp_file);" "output = net(X);"), X = X.T, tmp_file = tmp_file) return results['output']
def sensitivity(self, X): tmp_file = path.join(self.tmp_dir,'net.mat') results = matlab_command(("load(tmp_file);" "Savg = ffnnSensitivityAnalysis(net,inputs);"), inputs = X.T, tmp_file = tmp_file) return results['Savg']
def predict_proba(self, X): tmp_file = path.join(self.tmp_dir,'net.mat') results = matlab_command(("load(tmp_file);" "y = net(X);"), X = X.T, tmp_file = tmp_file) return results['y'].T
def fit(self, X, y): targets = count_to_targets(y) tmp_file = path.join(self.tmp_dir,'net.mat') results = matlab_command(("net = fitNN(X,y);" "save(tmp_file,'net');"), X = X.T, y = targets.T, tmp_file = tmp_file) self.coef_ = self.sensitivity(X) return self