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SampleMultivariateGaussian.make_node()   A
last analyzed

↳ Parent: SampleMultivariateGaussian

Complexity

Conditions 3

Size

Total Lines 6

Duplication

Lines 0
Ratio 0 %

Importance

Changes 1
Bugs 0 Features 0
Metric Value
cc 3
c 1
b 0
f 0
dl 0
loc 6
rs 9.4285
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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import numpy as np
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from theano import gof, tensor
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import theano
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import theano.tensor as T
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class SampleMultivariateGaussian(gof.Op):

            


            
                                                                                                            
                            
            
                                    
            
            

                
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    __props__ = ()

            


            
                                                                                                            
                                                                
            
                                    
            
            

                
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    def make_node(self, x, cov):

            


            
                                                                        
                            
            
                                    
            
            

                
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        if x.type.ndim != 1:

            


            
                                                                        
                            
            
                                    
            
            

                
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            raise TypeError('x must be a 1-d vector')

            


            
                                                                        
                            
            
                                    
            
            

                
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        if cov.type.ndim != 2:

            


            
                                                                        
                            
            
                                    
            
            

                
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            raise TypeError('cov must be a 2-d matrix')

            


            
                                                                        
                            
            
                                    
            
            

                
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        return gof.Apply(self, [x, cov], [T.vector(dtype="float32")])

            


            
                                                                                                            
                            
            
                                    
            
            

                
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    def perform(self, node, inp, out):

            


            
                                                                                                            
                            
            
                                    
            
            

                
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        x, cov = inp

            


            
                                                                                                            
                            
            
                                    
            
            

                
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        z, = out

            


            
                                                                                                            
                            
            
                                    
            
            

                
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        z[0] = np.random.multivariate_normal(x, cov, 1)[0].astype("float32")

            


            
                                                                                                            
                            
            
                                    
            
            

                
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    def grad(self, inputs, outputs):

            


            
                                                                                                            
                            
            
                                    
            
            

                
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        return [outputs[0], T.zeros_like(inputs[1])]

            


            
                                                                                                            
                            
            
                                    
            
            

                
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    def R_op(self, inputs, eval_points):

            


            
                                                                                                            
                            
            
                                    
            
            

                
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        if eval_points[0] is None:

            


            
                                                                                                            
                                                                
            
                                    
            
            

                
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            return eval_points

            


            
                                                        
            
                                    
            
            

                
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        return self.grad(inputs, eval_points)