def trainModel(self, x_data, penalty=1, kernel=Kernels.defaultKernel(),
eta=1, report=False):
y = x_data[:, 0]
# add a column of ones for the intercept
X = np.hstack((np.ones((len(x_data), 1)), x_data[:, 1:]))
weights = self._trainSVM(X, y, penalty, eta, report, kernel)
return self.packModel(weights, penalty, kernel)
def trainModel(self, x_data, kernel=Kernels.defaultKernel(), report=False):
# +1 for intercept
self.weights = [0] * (x_data.shape[1] + 1)
y = x_data[:, 0]
# add a column of ones for the intercept
X = np.hstack((np.ones((len(x_data), 1)), x_data[:, 1:]))
mistakes = self._trainSVM(x_data[:, 1:], y, report, kernel)
# Returns length 785
print mistakes
return mistakes
