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问题:
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: #i=[2 2 2 ] #for example h=i*Weights #h=[[8 8 8][8 8 8][8 8 8]] All=np.concatenate((All,h),axis=0)
I ge this error:
ValueError: all the input arrays must have same number of dimensions
I want "All" variable to be
[[8 8 8][8 8 8][8 8 8] [12 12 12][12 12 12][12 12 12]]
Any way how I can add "h" to "All" through the loop ?
回答1:
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]])
回答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.
回答3:
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.
回答4:
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.
