class SoftmaxRegression(Objective):
def __init__(self,theta_df,X_df,y_df,reg):
""" Combine Softmax loss with a linear model for Softmax regression. """
self.X = X_df.get_matrix(readonly=True)
self.theta = theta_df.get_matrix()
self.yt = y_df.get_matrix(readonly=True)
self.yp = self.X.dot(self.theta.T)
self.loss = Softmax(self.yp,self.yt)
self.X_df = X_df
self.y_df = y_df
self.reg = reg
def f(self):
return np.mean(self.loss.f()) + self.reg/2*np.sum(self.theta**2)
def f_vec(self):
return self.loss.f() + self.reg/2*np.sum(self.theta**2)
def g(self):
n = self.X.shape[0]
return self.loss.g().T.dot(self.X)/n + self.reg*self.theta
def h(self):
# Needs to be averaged and also regularized
return self.loss.h()
def err(self):
d = self.X.ndim-1
return (self.yt.argmax(axis=d)!=self.yp.argmax(axis=d))
@staticmethod
def structure(X_df,y_df,reg):
return y_df.cols(), X_df.cols()
class SoftmaxRegression(Objective):
def __init__(self, theta_df, X_df, y_df, reg):
""" Combine Softmax loss with a linear model for Softmax regression. """
self.X = X_df.get_matrix(readonly=True)
self.theta = theta_df.get_matrix()
self.yt = y_df.get_matrix(readonly=True)
self.yp = self.X.dot(self.theta.T)
self.loss = Softmax(self.yp, self.yt)
self.X_df = X_df
self.y_df = y_df
self.reg = reg
def f(self):
return np.mean(self.loss.f()) + self.reg / 2 * np.sum(self.theta**2)
def f_vec(self):
return self.loss.f() + self.reg / 2 * np.sum(self.theta**2)
def g(self):
n = self.X.shape[0]
return self.loss.g().T.dot(self.X) / n + self.reg * self.theta
def h(self):
# Needs to be averaged and also regularized
return self.loss.h()
def err(self):
d = self.X.ndim - 1
return (self.yt.argmax(axis=d) != self.yp.argmax(axis=d))
@staticmethod
def structure(X_df, y_df, reg):
return y_df.cols(), X_df.cols()
def __init__(self, theta_df, X_df, y_df, reg):
""" Combine Softmax loss with a linear model for Softmax regression. """
self.X = X_df.get_matrix(readonly=True)
self.theta = theta_df.get_matrix()
self.yt = y_df.get_matrix(readonly=True)
self.yp = self.X.dot(self.theta.T)
self.loss = Softmax(self.yp, self.yt)
self.X_df = X_df
self.y_df = y_df
self.reg = reg
def __init__(self,theta_df,X_df,y_df,reg):
""" Combine Softmax loss with a linear model for Softmax regression. """
self.X = X_df.get_matrix(readonly=True)
self.theta = theta_df.get_matrix()
self.yt = y_df.get_matrix(readonly=True)
self.yp = self.X.dot(self.theta.T)
self.loss = Softmax(self.yp,self.yt)
self.X_df = X_df
self.y_df = y_df
self.reg = reg