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comparison pylearn/algorithms/kernel_regression.py @ 1504:bf5c0f797161

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Fix test.
author Frederic Bastien <nouiz@nouiz.org>
date Mon, 12 Sep 2011 10:48:33 -0400
parents 9b371879c6ab
children 723e2d761985
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1503:1ee532a6f33b 1504:bf5c0f797161
1 """ 1 """
2 Implementation of kernel regression: 2 Implementation of kernel regression:
3 """ 3 """
4 4
5 from pylearn.old_dataset.learner import OfflineLearningAlgorithm 5 #from pylearn.old_dataset.learner import OfflineLearningAlgorithm
6 from theano import tensor as T 6 from theano import tensor as T
7 from theano.tensor.nnet import prepend_1_to_each_row 7 from theano.tensor.nnet import prepend_1_to_each_row
8 from theano.scalar import as_scalar 8 from theano.scalar import as_scalar
9 from common.autoname import AutoName 9 from common.autoname import AutoName
10 import theano 10 import theano
13 # map a N-vector to a 1xN matrix 13 # map a N-vector to a 1xN matrix
14 row_vector = theano.tensor.DimShuffle((False,),['x',0]) 14 row_vector = theano.tensor.DimShuffle((False,),['x',0])
15 # map a N-vector to a Nx1 matrix 15 # map a N-vector to a Nx1 matrix
16 col_vector = theano.tensor.DimShuffle((False,),[0,'x']) 16 col_vector = theano.tensor.DimShuffle((False,),[0,'x'])
17 17
18 class KernelRegression(OfflineLearningAlgorithm): 18 class KernelRegression():#OfflineLearningAlgorithm):
19 """ 19 """
20 Implementation of kernel regression: 20 Implementation of kernel regression:
21 * the data are n (x_t,y_t) pairs and we want to estimate E[y|x] 21 * the data are n (x_t,y_t) pairs and we want to estimate E[y|x]
22 * the predictor computes 22 * the predictor computes
23 f(x) = b + \sum_{t=1}^n \alpha_t K(x,x_t) 23 f(x) = b + \sum_{t=1}^n \alpha_t K(x,x_t)