rpy2 how to call as.matrix()












0















I am using rpy2 to call some R function from python. The R function returns a dist object. How do I call as.matrix() from python on the result the R code returns?



import rpy2.robjects.numpy2ri

from rpy2.robjects.packages import importr

rpy2.robjects.numpy2ri.activate()

Rsession = rpy2.robjects.r

tsclust = importr('TSclust')



X= np.random.random((5,5))

result = Rsession.diss( X , "ACF", p=0.05)


result is of the class dist. I would like a square distance matrix, which I can achieve by calling as.matrix() on the result . How do I do this with rpy2?






rpy2







share|improve this question



























    0















    I am using rpy2 to call some R function from python. The R function returns a dist object. How do I call as.matrix() from python on the result the R code returns?



    import rpy2.robjects.numpy2ri
    
from rpy2.robjects.packages import importr
    
rpy2.robjects.numpy2ri.activate()
    
Rsession = rpy2.robjects.r
    
tsclust = importr('TSclust')
    

    
X= np.random.random((5,5))
    
result = Rsession.diss( X , "ACF", p=0.05)


    result is of the class dist. I would like a square distance matrix, which I can achieve by calling as.matrix() on the result . How do I do this with rpy2?






    rpy2







    share|improve this question

























      0












      0








      0








      I am using rpy2 to call some R function from python. The R function returns a dist object. How do I call as.matrix() from python on the result the R code returns?



      import rpy2.robjects.numpy2ri
      
from rpy2.robjects.packages import importr
      
rpy2.robjects.numpy2ri.activate()
      
Rsession = rpy2.robjects.r
      
tsclust = importr('TSclust')
      

      
X= np.random.random((5,5))
      
result = Rsession.diss( X , "ACF", p=0.05)


      result is of the class dist. I would like a square distance matrix, which I can achieve by calling as.matrix() on the result . How do I do this with rpy2?






      rpy2







      share|improve this question














      I am using rpy2 to call some R function from python. The R function returns a dist object. How do I call as.matrix() from python on the result the R code returns?



      import rpy2.robjects.numpy2ri
      
from rpy2.robjects.packages import importr
      
rpy2.robjects.numpy2ri.activate()
      
Rsession = rpy2.robjects.r
      
tsclust = importr('TSclust')
      

      
X= np.random.random((5,5))
      
result = Rsession.diss( X , "ACF", p=0.05)


      result is of the class dist. I would like a square distance matrix, which I can achieve by calling as.matrix() on the result . How do I do this with rpy2?






      rpy2




      rpy2






      share|improve this question













      share|improve this question











      share|improve this question




      share|improve this question










      asked Nov 21 '18 at 18:54









      benbobenbo

      652619




      652619
























          1 Answer
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          0














          I figured out one solution. It's not pretty but it works. We define a custom function that calls as.matrix



          from rpy2.robjects.packages import STAP
          
mfunc = 'myasmatrix <- function(dobj){return(as.matrix(dobj))}'
          
myasmatrix = STAP(mfunc, "myasmatrix")
          

          
np.array(myasmatrix.myasmatrix(rres))
          
>>> array([[0.        , 0.0663193 , 0.01159857, 0.14378692, 0.10069314],
          
   [0.0663193 , 0.        , 0.06064907, 0.07965314, 0.03511945],
          
   [0.01159857, 0.06064907, 0.        , 0.13898505, 0.095319  ],
          
   [0.14378692, 0.07965314, 0.13898505, 0.        , 0.04757353],
          
   [0.10069314, 0.03511945, 0.095319  , 0.04757353, 0.        ]])





          share|improve this answer























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            1 Answer
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            1 Answer
            1






            active

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            active

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            active

            oldest

            votes









            0














            I figured out one solution. It's not pretty but it works. We define a custom function that calls as.matrix



            from rpy2.robjects.packages import STAP
            
mfunc = 'myasmatrix <- function(dobj){return(as.matrix(dobj))}'
            
myasmatrix = STAP(mfunc, "myasmatrix")
            

            
np.array(myasmatrix.myasmatrix(rres))
            
>>> array([[0.        , 0.0663193 , 0.01159857, 0.14378692, 0.10069314],
            
   [0.0663193 , 0.        , 0.06064907, 0.07965314, 0.03511945],
            
   [0.01159857, 0.06064907, 0.        , 0.13898505, 0.095319  ],
            
   [0.14378692, 0.07965314, 0.13898505, 0.        , 0.04757353],
            
   [0.10069314, 0.03511945, 0.095319  , 0.04757353, 0.        ]])





            share|improve this answer




























              0














              I figured out one solution. It's not pretty but it works. We define a custom function that calls as.matrix



              from rpy2.robjects.packages import STAP
              
mfunc = 'myasmatrix <- function(dobj){return(as.matrix(dobj))}'
              
myasmatrix = STAP(mfunc, "myasmatrix")
              

              
np.array(myasmatrix.myasmatrix(rres))
              
>>> array([[0.        , 0.0663193 , 0.01159857, 0.14378692, 0.10069314],
              
   [0.0663193 , 0.        , 0.06064907, 0.07965314, 0.03511945],
              
   [0.01159857, 0.06064907, 0.        , 0.13898505, 0.095319  ],
              
   [0.14378692, 0.07965314, 0.13898505, 0.        , 0.04757353],
              
   [0.10069314, 0.03511945, 0.095319  , 0.04757353, 0.        ]])





              share|improve this answer


























                0












                0








                0







                I figured out one solution. It's not pretty but it works. We define a custom function that calls as.matrix



                from rpy2.robjects.packages import STAP
                
mfunc = 'myasmatrix <- function(dobj){return(as.matrix(dobj))}'
                
myasmatrix = STAP(mfunc, "myasmatrix")
                

                
np.array(myasmatrix.myasmatrix(rres))
                
>>> array([[0.        , 0.0663193 , 0.01159857, 0.14378692, 0.10069314],
                
   [0.0663193 , 0.        , 0.06064907, 0.07965314, 0.03511945],
                
   [0.01159857, 0.06064907, 0.        , 0.13898505, 0.095319  ],
                
   [0.14378692, 0.07965314, 0.13898505, 0.        , 0.04757353],
                
   [0.10069314, 0.03511945, 0.095319  , 0.04757353, 0.        ]])





                share|improve this answer













                I figured out one solution. It's not pretty but it works. We define a custom function that calls as.matrix



                from rpy2.robjects.packages import STAP
                
mfunc = 'myasmatrix <- function(dobj){return(as.matrix(dobj))}'
                
myasmatrix = STAP(mfunc, "myasmatrix")
                

                
np.array(myasmatrix.myasmatrix(rres))
                
>>> array([[0.        , 0.0663193 , 0.01159857, 0.14378692, 0.10069314],
                
   [0.0663193 , 0.        , 0.06064907, 0.07965314, 0.03511945],
                
   [0.01159857, 0.06064907, 0.        , 0.13898505, 0.095319  ],
                
   [0.14378692, 0.07965314, 0.13898505, 0.        , 0.04757353],
                
   [0.10069314, 0.03511945, 0.095319  , 0.04757353, 0.        ]])






                share|improve this answer












                share|improve this answer



                share|improve this answer










                answered Nov 21 '18 at 19:05









                benbobenbo

                652619




                652619
































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