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some work on the linear mapping

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This commit is contained in:
James Hensman 2014-03-20 12:22:57 +00:00
parent 6f9c97ee72
commit dfe325b571
2 changed files with 15 additions and 30 deletions
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41
GPy/mappings/linear.py
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@ -3,6 +3,7 @@
import numpy as np
from ..core.mapping import Mapping
from ..core.parameterization import Param
class Linear(Mapping):
"""
@ -16,38 +17,22 @@ class Linear(Mapping):
:type X: ndarray
:param output_dim: dimension of output.
:type output_dim: int
"""
def __init__(self, input_dim=1, output_dim=1):
self.name = 'linear'
Mapping.__init__(self, input_dim=input_dim, output_dim=output_dim)
self.num_params = self.output_dim*(self.input_dim + 1)
self.W = np.array((self.input_dim, self.output_dim))
self.bias = np.array(self.output_dim)
self.randomize()
def _get_param_names(self):
return sum([['W_%i_%i' % (n, d) for d in range(self.output_dim)] for n in range(self.input_dim)], []) + ['bias_%i' % d for d in range(self.output_dim)]
def _get_params(self):
return np.hstack((self.W.flatten(), self.bias))
def _set_params(self, x):
self.W = x[:self.input_dim * self.output_dim].reshape(self.input_dim, self.output_dim).copy()
self.bias = x[self.input_dim*self.output_dim:].copy()
def randomize(self):
self.W = np.random.randn(self.input_dim, self.output_dim)/np.sqrt(self.input_dim + 1)
self.bias = np.random.randn(self.output_dim)/np.sqrt(self.input_dim + 1)
def __init__(self, input_dim=1, output_dim=1, name='linear_map'):
Mapping.__init__(self, input_dim=input_dim, output_dim=output_dim, name=name)
self.W = Param('W',np.array((self.input_dim, self.output_dim)))
self.bias = Param('bias',np.array(self.output_dim))
self.add_parameters(self.W, self.bias)
def f(self, X):
return np.dot(X,self.W) + self.bias
def df_dtheta(self, dL_df, X):
self._df_dW = (dL_df[:, :, None]*X[:, None, :]).sum(0).T
self._df_dbias = (dL_df.sum(0))
return np.hstack((self._df_dW.flatten(), self._df_dbias))
def df_dX(self, dL_df, X):
return (dL_df[:, None, :]*self.W[None, :, :]).sum(2)
df_dW = (dL_df[:, :, None]*X[:, None, :]).sum(0).T
df_dbias = (dL_df.sum(0))
return np.hstack((df_dW.flatten(), df_dbias))
def dL_dX(self, dL_df, X):
return (dL_df[:, None, :]*self.W[None, :, :]).sum(2)