how to append a numpy matrix into an empty numpy array

Question

I want to append a numpy array(matrix) into an array through a loop

data=[[2 2 2] [3 3 3]]
Weights=[[4 4 4] [4 4 4] [4 4 4]]
All=np.array([])  
for i in data:
           


        

Answer 1

It may not be the best solution but it seems to work.

data = np.array([[2, 2, 2], [3, 3, 3]])
Weights = np.array([[4, 4, 4], [4, 4, 4], [4, 4, 4]])
All = []

for i in data:
    for j in Weights:
        h = i * j
        All.append(h)

All = np.array(All)

I'd like to say it's not the best solution because it appends the result to a list and at the end converts the list in a numpy array but it works good for small applications. I mean if you have to do heavy calculations like this it's i would consider finding another method. Anyway with this method you don't have to think about the number conversions from floating point. Hope this helps.

Answer 2

Adam, how about just using a pair of nested loops? I believe this code will do what you want.

import numpy as np
data = ([2,2,2],[3,3,3])
weights = ([4,4,4],[4,4,4],[4,4,4])

output=np.array([])
for each_array in data:
    for weight in weights:
            each_multiplication = np.multiply(each_array, weight)
            output = np.append(output,each_multiplication)

print output

np.multiply() performs element wise multiplication instead of matrix multiplication. As best as I can understand from your sample input and output, this is what you're trying to accomplish.

Answer 3

A preferred way of constructing an array with a loop is to collect values in a list, and perform the concatenate once, at the end:

In [1025]: data
Out[1025]: 
array([[2, 2, 2],
       [3, 3, 3]])
In [1026]: Weights
Out[1026]: 
array([[4, 4, 4],
       [4, 4, 4],
       [4, 4, 4]])

Append to a list is much faster than repeated concatenate; plus it avoids the 'empty` array shape issue:

In [1027]: alist=[]
In [1028]: for row in data:
      ...:     alist.append(row*Weights)
In [1029]: alist
Out[1029]: 
[array([[8, 8, 8],
        [8, 8, 8],
        [8, 8, 8]]), array([[12, 12, 12],
        [12, 12, 12],
        [12, 12, 12]])]

In [1031]: np.concatenate(alist,axis=0)
Out[1031]: 
array([[ 8,  8,  8],
       [ 8,  8,  8],
       [ 8,  8,  8],
       [12, 12, 12],
       [12, 12, 12],
       [12, 12, 12]])

You can also join the arrays on a new dimension with np.array or np.stack:

In [1032]: np.array(alist)
Out[1032]: 
array([[[ 8,  8,  8],
        [ 8,  8,  8],
        [ 8,  8,  8]],

       [[12, 12, 12],
        [12, 12, 12],
        [12, 12, 12]]])
In [1033]: _.shape
Out[1033]: (2, 3, 3)

I can construct this 3d version with a simple broadcasted multiplication - no loops

In [1034]: data[:,None,:]*Weights[None,:,:]
Out[1034]: 
array([[[ 8,  8,  8],
        [ 8,  8,  8],
        [ 8,  8,  8]],

       [[12, 12, 12],
        [12, 12, 12],
        [12, 12, 12]]])

Add a .reshape(-1,3) to that to get the (6,3) version.

np.repeat(data,3,axis=0)*np.tile(Weights,[2,1]) also produces the desired 6x3 array.

Answer 4

Option 1: Reshape your initial All array to 3 columns so that the number of columns match h:

All=np.array([]).reshape((0,3))

for i in data:
    h=i*Weights      
    All=np.concatenate((All,h))

All
#array([[  8.,   8.,   8.],
#       [  8.,   8.,   8.],
#       [  8.,   8.,   8.],
#       [ 12.,  12.,  12.],
#       [ 12.,  12.,  12.],
#       [ 12.,  12.,  12.]])

Option 2: Use a if-else statement to handle initial empty array case:

All=np.array([])
for i in data:
    h=i*Weights      
    if len(All) == 0:
        All = h
    else:
        All=np.concatenate((All,h))

All
#array([[ 8,  8,  8],
#       [ 8,  8,  8],
#       [ 8,  8,  8],
#       [12, 12, 12],
#       [12, 12, 12],
#       [12, 12, 12]])

Option 3: Use itertools.product():

import itertools
np.array([i*j for i,j in itertools.product(data, Weights)])

#array([[ 8,  8,  8],
#       [ 8,  8,  8],
#       [ 8,  8,  8],
#       [12, 12, 12],
#       [12, 12, 12],
#       [12, 12, 12]])