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Fixed gplvm unittest which was sampling an output matrix of size input_dim (and corresponding error in bcgplvm unittest which was based on it).

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This commit is contained in:
Neil Lawrence 2013-08-29 19:57:25 +02:00
parent 128ebbabc5
commit bdc8ee45a7
4 changed files with 13 additions and 13 deletions
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2
GPy/mappings/__init__.py
View file

@ -1,7 +1,7 @@
# Copyright (c) 2013, GPy authors (see AUTHORS.txt).
# Licensed under the BSD 3-clause license (see LICENSE.txt)
#from kernel import Kernel
from kernel import Kernel
from linear import Linear
from mlp import MLP
#from rbf import RBF

4
GPy/mappings/kernel.py
View file

@ -3,7 +3,7 @@
import numpy as np
from ..core.mapping import Mapping
from ..kern.kern import kern
import GPy
class Kernel(Mapping):
"""
@ -25,7 +25,7 @@ class Kernel(Mapping):
def __init__(self, X, output_dim=1, kernel=None):
Mapping.__init__(self, input_dim=X.shape[1], output_dim=output_dim)
if kernel is None:
kernel = kern.rbf(self.input_dim)
kernel = GPy.kern.rbf(self.input_dim)
self.kern = kernel
self.X = X
self.num_data = X.shape[0]

2
GPy/mappings/mlp.py
View file

@ -89,6 +89,7 @@ class MLP(Mapping):
def df_dtheta(self, dL_df, X):
self._df_computations(dL_df, X)
g = np.array([])
for gW, gbias in zip(self._dL_dW, self._dL_dbias):
g = np.hstack((g, gW.flatten(), gbias))
return g
@ -117,7 +118,6 @@ class MLP(Mapping):
self._dL_dW[0] = (dL_da[:, :, None]*X[:, None, :]).sum(0).T
self._dL_dbias[0] = (dL_da.sum(0))
self._dL_dX = (dL_da[:, None, :]*W[None, :, :]).sum(2)
g = np.array([])
def df_dX(self, dL_df, X):

18
GPy/testing/gplvm_tests.py
View file

@ -7,33 +7,33 @@ import GPy
class GPLVMTests(unittest.TestCase):
def test_bias_kern(self):
N, num_inducing, input_dim, D = 10, 3, 2, 4
X = np.random.rand(N, input_dim)
num_data, num_inducing, input_dim, output_dim = 10, 3, 2, 4
X = np.random.rand(num_data, input_dim)
k = GPy.kern.rbf(input_dim) + GPy.kern.white(input_dim, 0.00001)
K = k.K(X)
Y = np.random.multivariate_normal(np.zeros(N),K,input_dim).T
Y = np.random.multivariate_normal(np.zeros(num_data),K,output_dim).T
k = GPy.kern.bias(input_dim) + GPy.kern.white(input_dim, 0.00001)
m = GPy.models.GPLVM(Y, input_dim, kernel = k)
m.randomize()
self.assertTrue(m.checkgrad())
def test_linear_kern(self):
N, num_inducing, input_dim, D = 10, 3, 2, 4
X = np.random.rand(N, input_dim)
num_data, num_inducing, input_dim, output_dim = 10, 3, 2, 4
X = np.random.rand(num_data, input_dim)
k = GPy.kern.rbf(input_dim) + GPy.kern.white(input_dim, 0.00001)
K = k.K(X)
Y = np.random.multivariate_normal(np.zeros(N),K,input_dim).T
Y = np.random.multivariate_normal(np.zeros(num_data),K,output_dim).T
k = GPy.kern.linear(input_dim) + GPy.kern.white(input_dim, 0.00001)
m = GPy.models.GPLVM(Y, input_dim, kernel = k)
m.randomize()
self.assertTrue(m.checkgrad())
def test_rbf_kern(self):
N, num_inducing, input_dim, D = 10, 3, 2, 4
X = np.random.rand(N, input_dim)
num_data, num_inducing, input_dim, output_dim = 10, 3, 2, 4
X = np.random.rand(num_data, input_dim)
k = GPy.kern.rbf(input_dim) + GPy.kern.white(input_dim, 0.00001)
K = k.K(X)
Y = np.random.multivariate_normal(np.zeros(N),K,input_dim).T
Y = np.random.multivariate_normal(np.zeros(num_data),K,output_dim).T
k = GPy.kern.rbf(input_dim) + GPy.kern.white(input_dim, 0.00001)
m = GPy.models.GPLVM(Y, input_dim, kernel = k)
m.randomize()