def _grad(self, values): """Gives the (sub/super)gradient of the atom w.r.t. each argument. Matrix expressions are vectorized, so the gradient is a matrix. Args: values: A list of numeric values for the arguments. Returns: A list of SciPy CSC sparse matrices or None. """ rows = self.args[0].size[0]*self.args[0].size[1] cols = self.size[0]*self.size[1] if self.p == 0: # All zeros. return [sp.csc_matrix((rows, cols), dtype='float64')] # Outside domain or on boundary. if not is_power2(self.p) and np.min(values[0]) <= 0: if self.p < 1: # Non-differentiable. return [None] else: # Round up to zero. values[0] = np.maximum(values[0], 0) grad_vals = self.p*np.power(values[0], self.p-1) return [Elementwise.elemwise_grad_to_diag(grad_vals, rows, cols)]
def _grad(self, values): """Gives the (sub/super)gradient of the atom w.r.t. each argument. Matrix expressions are vectorized, so the gradient is a matrix. Args: values: A list of numeric values for the arguments. Returns: A list of SciPy CSC sparse matrices or None. """ rows = self.args[0].size[0] * self.args[0].size[1] cols = self.size[0] * self.size[1] if self.p == 0: # All zeros. return [sp.csc_matrix((rows, cols), dtype='float64')] # Outside domain or on boundary. if not is_power2(self.p) and np.min(values[0]) <= 0: if self.p < 1: # Non-differentiable. return [None] else: # Round up to zero. values[0] = np.maximum(values[0], 0) grad_vals = self.p * np.power(values[0], self.p - 1) return [Elementwise.elemwise_grad_to_diag(grad_vals, rows, cols)]
def _grad(self, values): """Gives the (sub/super)gradient of the atom w.r.t. each argument. Matrix expressions are vectorized, so the gradient is a matrix. Args: values: A list of numeric values for the arguments. Returns: A list of SciPy CSC sparse matrices or None. """ rows = self.args[0].size[0] * self.args[0].size[1] cols = self.size[0] * self.size[1] # Outside domain or on boundary. if np.min(values[0]) <= 0: # Non-differentiable. return [None] else: grad_vals = 1.0 / values[0] return [Elementwise.elemwise_grad_to_diag(grad_vals, rows, cols)]
def _grad(self, values): """Gives the (sub/super)gradient of the atom w.r.t. each argument. Matrix expressions are vectorized, so the gradient is a matrix. Args: values: A list of numeric values for the arguments. Returns: A list of SciPy CSC sparse matrices or None. """ rows = self.args[0].size[0]*self.args[0].size[1] cols = self.size[0]*self.size[1] # Outside domain or on boundary. if np.min(values[0]) <= 0: # Non-differentiable. return [None] else: grad_vals = 1.0/values[0] return [Elementwise.elemwise_grad_to_diag(grad_vals, rows, cols)]