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
