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Tensorflow mix two multivariate distribution

1

I would like to mix two multivariate distribution in tensorflow. For example:

import tensorflow_probability as tfp
import tensorflow as tf
import numpy as np
tfd = tfp.distributions

#mean,var,pi have the same shape(3,4).
mean = tf.convert_to_tensor(np.arange(12.0).reshape(3,4))
var = mean
dist = tfd.Normal(loc=mean,scale=var)
pi = tf.ones_like(mean)
mix = tfd.Mixture(cat = tfd.Categorical(probs=[pi,1-pi]),components=[dist,dist])

However, it got the error as follows:

ValueError: Dimensions 2 and 3 are not compatible

ValueError: Shapes (2, 3) and (3, 4) are not compatible

Can I mix two multivariate distribution in tensorflow?

python tensorflow tensorflow-probability

share|improve this question

edited Mar 21 at 18:23

MPękalski

2,14511729

asked Mar 21 at 16:46

MozzieMozzie

868

add a comment | 

1

I would like to mix two multivariate distribution in tensorflow. For example:

import tensorflow_probability as tfp
import tensorflow as tf
import numpy as np
tfd = tfp.distributions

#mean,var,pi have the same shape(3,4).
mean = tf.convert_to_tensor(np.arange(12.0).reshape(3,4))
var = mean
dist = tfd.Normal(loc=mean,scale=var)
pi = tf.ones_like(mean)
mix = tfd.Mixture(cat = tfd.Categorical(probs=[pi,1-pi]),components=[dist,dist])

However, it got the error as follows:

ValueError: Dimensions 2 and 3 are not compatible

ValueError: Shapes (2, 3) and (3, 4) are not compatible

Can I mix two multivariate distribution in tensorflow?

python tensorflow tensorflow-probability

share|improve this question

edited Mar 21 at 18:23

MPękalski

2,14511729

asked Mar 21 at 16:46

MozzieMozzie

868

add a comment | 

I would like to mix two multivariate distribution in tensorflow. For example:

import tensorflow_probability as tfp
import tensorflow as tf
import numpy as np
tfd = tfp.distributions

#mean,var,pi have the same shape(3,4).
mean = tf.convert_to_tensor(np.arange(12.0).reshape(3,4))
var = mean
dist = tfd.Normal(loc=mean,scale=var)
pi = tf.ones_like(mean)
mix = tfd.Mixture(cat = tfd.Categorical(probs=[pi,1-pi]),components=[dist,dist])

However, it got the error as follows:

ValueError: Dimensions 2 and 3 are not compatible

ValueError: Shapes (2, 3) and (3, 4) are not compatible

Can I mix two multivariate distribution in tensorflow?

python tensorflow tensorflow-probability

share|improve this question

edited Mar 21 at 18:23

MPękalski

2,14511729

asked Mar 21 at 16:46

MozzieMozzie

868

import tensorflow_probability as tfp
import tensorflow as tf
import numpy as np
tfd = tfp.distributions

#mean,var,pi have the same shape(3,4).
mean = tf.convert_to_tensor(np.arange(12.0).reshape(3,4))
var = mean
dist = tfd.Normal(loc=mean,scale=var)
pi = tf.ones_like(mean)
mix = tfd.Mixture(cat = tfd.Categorical(probs=[pi,1-pi]),components=[dist,dist])

share|improve this question

edited Mar 21 at 18:23

MPękalski

2,14511729

asked Mar 21 at 16:46

MozzieMozzie

868

share|improve this question

edited Mar 21 at 18:23

MPękalski

2,14511729

asked Mar 21 at 16:46

MozzieMozzie

868

edited Mar 21 at 18:23

MPękalski

2,14511729

edited Mar 21 at 18:23

MPękalski

2,14511729

asked Mar 21 at 16:46

MozzieMozzie

868

asked Mar 21 at 16:46

MozzieMozzie

868

1 Answer
1

active

oldest

votes

1

Try if this solves your issue

import numpy as np
import tensorflow as tf
import tensorflow_probability as tfp
tfd = tfp.distributions 

#mean,var,pi have the same shape(3,4).
mean = tf.convert_to_tensor(np.arange(12.0).reshape(3,4))
var = mean
dist = tfd.Normal(loc=-1., scale=0.1)

pi = tf.transpose(tf.ones_like(mean))

mix = tfd.Mixture(cat = tfd.Categorical(probs=[pi/3,
 pi/3,
 pi/3]), 
 components=[tfd.Normal(loc=mean,scale=var), 
 tfd.Normal(loc=mean,scale=var), 
 tfd.Normal(loc=mean,scale=var)]
 )

mix.event_shape_tensor

output

<bound method Distribution.event_shape_tensor of <tfp.distributions.Mixture 'Mixture_11/' batch_shape=(3, 4) event_shape=() dtype=float64>>

share|improve this answer

answered Mar 21 at 18:56

MPękalskiMPękalski

2,14511729

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Thanks for your help. It seems that it does not work if the components contain only two distributions. I'm not sure why it happens. mix = tfd.Mixture(cat = tfd.Categorical(probs=[pi/2,pi/2]),components=[dist,dist]). Thanks anyway.
  
  – Mozzie
  Mar 22 at 11:20
  
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  I think it might be because your mean/var are not square matrices.
  
  – MPękalski
  Mar 22 at 12:55
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  I think you are right. I found another solution for this. Instead of assigning the probability to pi #shape(3,4), I assign it to pi #shape(3,4,2). That means each element of the pi is a probability list eg. pi[0][0] = [0.5,0.5]. That works. Thanks a lot.
  
  – Mozzie
  Mar 22 at 13:36
  

  
  
  
  

add a comment | 

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1

Try if this solves your issue

import numpy as np
import tensorflow as tf
import tensorflow_probability as tfp
tfd = tfp.distributions 

#mean,var,pi have the same shape(3,4).
mean = tf.convert_to_tensor(np.arange(12.0).reshape(3,4))
var = mean
dist = tfd.Normal(loc=-1., scale=0.1)

pi = tf.transpose(tf.ones_like(mean))

mix = tfd.Mixture(cat = tfd.Categorical(probs=[pi/3,
 pi/3,
 pi/3]), 
 components=[tfd.Normal(loc=mean,scale=var), 
 tfd.Normal(loc=mean,scale=var), 
 tfd.Normal(loc=mean,scale=var)]
 )

mix.event_shape_tensor

output

<bound method Distribution.event_shape_tensor of <tfp.distributions.Mixture 'Mixture_11/' batch_shape=(3, 4) event_shape=() dtype=float64>>

share|improve this answer

answered Mar 21 at 18:56

MPękalskiMPękalski

2,14511729

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Thanks for your help. It seems that it does not work if the components contain only two distributions. I'm not sure why it happens. mix = tfd.Mixture(cat = tfd.Categorical(probs=[pi/2,pi/2]),components=[dist,dist]). Thanks anyway.
  
  – Mozzie
  Mar 22 at 11:20
  
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  I think it might be because your mean/var are not square matrices.
  
  – MPękalski
  Mar 22 at 12:55
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  I think you are right. I found another solution for this. Instead of assigning the probability to pi #shape(3,4), I assign it to pi #shape(3,4,2). That means each element of the pi is a probability list eg. pi[0][0] = [0.5,0.5]. That works. Thanks a lot.
  
  – Mozzie
  Mar 22 at 13:36
  

  
  
  
  

add a comment | 

1

Try if this solves your issue

import numpy as np
import tensorflow as tf
import tensorflow_probability as tfp
tfd = tfp.distributions 

#mean,var,pi have the same shape(3,4).
mean = tf.convert_to_tensor(np.arange(12.0).reshape(3,4))
var = mean
dist = tfd.Normal(loc=-1., scale=0.1)

pi = tf.transpose(tf.ones_like(mean))

mix = tfd.Mixture(cat = tfd.Categorical(probs=[pi/3,
 pi/3,
 pi/3]), 
 components=[tfd.Normal(loc=mean,scale=var), 
 tfd.Normal(loc=mean,scale=var), 
 tfd.Normal(loc=mean,scale=var)]
 )

mix.event_shape_tensor

output

<bound method Distribution.event_shape_tensor of <tfp.distributions.Mixture 'Mixture_11/' batch_shape=(3, 4) event_shape=() dtype=float64>>

share|improve this answer

answered Mar 21 at 18:56

MPękalskiMPękalski

2,14511729

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Thanks for your help. It seems that it does not work if the components contain only two distributions. I'm not sure why it happens. mix = tfd.Mixture(cat = tfd.Categorical(probs=[pi/2,pi/2]),components=[dist,dist]). Thanks anyway.
  
  – Mozzie
  Mar 22 at 11:20
  
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  I think it might be because your mean/var are not square matrices.
  
  – MPękalski
  Mar 22 at 12:55
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  I think you are right. I found another solution for this. Instead of assigning the probability to pi #shape(3,4), I assign it to pi #shape(3,4,2). That means each element of the pi is a probability list eg. pi[0][0] = [0.5,0.5]. That works. Thanks a lot.
  
  – Mozzie
  Mar 22 at 13:36
  

  
  
  
  

add a comment | 

Try if this solves your issue

import numpy as np
import tensorflow as tf
import tensorflow_probability as tfp
tfd = tfp.distributions 

#mean,var,pi have the same shape(3,4).
mean = tf.convert_to_tensor(np.arange(12.0).reshape(3,4))
var = mean
dist = tfd.Normal(loc=-1., scale=0.1)

pi = tf.transpose(tf.ones_like(mean))

mix = tfd.Mixture(cat = tfd.Categorical(probs=[pi/3,
 pi/3,
 pi/3]), 
 components=[tfd.Normal(loc=mean,scale=var), 
 tfd.Normal(loc=mean,scale=var), 
 tfd.Normal(loc=mean,scale=var)]
 )

mix.event_shape_tensor

output

<bound method Distribution.event_shape_tensor of <tfp.distributions.Mixture 'Mixture_11/' batch_shape=(3, 4) event_shape=() dtype=float64>>

share|improve this answer

answered Mar 21 at 18:56

MPękalskiMPękalski

2,14511729

import numpy as np
import tensorflow as tf
import tensorflow_probability as tfp
tfd = tfp.distributions 

#mean,var,pi have the same shape(3,4).
mean = tf.convert_to_tensor(np.arange(12.0).reshape(3,4))
var = mean
dist = tfd.Normal(loc=-1., scale=0.1)

pi = tf.transpose(tf.ones_like(mean))

mix = tfd.Mixture(cat = tfd.Categorical(probs=[pi/3,
 pi/3,
 pi/3]), 
 components=[tfd.Normal(loc=mean,scale=var), 
 tfd.Normal(loc=mean,scale=var), 
 tfd.Normal(loc=mean,scale=var)]
 )

mix.event_shape_tensor

<bound method Distribution.event_shape_tensor of <tfp.distributions.Mixture 'Mixture_11/' batch_shape=(3, 4) event_shape=() dtype=float64>>

share|improve this answer

answered Mar 21 at 18:56

MPękalskiMPękalski

2,14511729

answered Mar 21 at 18:56

MPękalskiMPękalski

2,14511729

answered Mar 21 at 18:56

MPękalskiMPękalski

2,14511729