2D numpy数组不会从int64隐式转换为float64

Question

How come when the numpy array is a vector, the setting works and the dtype is implicitly converted to float but when the numpy array is a matrix, the setting works but the dtype is still int. Here's a demo script to illustrate the problem.

import numpy as np

# successfully sets / converts
x = np.array([100, 101])
c = -np.max(x)
x += c
print 'before', x.dtype
x = np.exp(x)
print 'after', x.dtype

print x

# doesn't successfully set / convert
matrix = np.array([(100, 101), (102, 103)])
for i in range(len(matrix)):
    c = -np.max(matrix[i])
    matrix[i] += c
    print 'before', matrix[i].dtype
    matrix[i] = np.exp(matrix[i])
    print 'after', matrix[i].dtype

print matrix

output:

before int64
after float64 <-- from vector
[ 0.36787944  1.        ]
before int64
after int64 <-- from row 1 of matrix
before int64
after int64 <-- from row 2 of matrix
[[0 1]
 [0 1]]

The numbers are integer truncated, which was my original problem, traced down to this.

I'm using Python 2.7.11 and numpy 1.13.0

Answer 1

Whenever you write a value into an existing array, the value is cast to match the array dtype . In your case, the resulting float64 value is cast to int64 :

b = numpy.arange(4).reshape(2, 2)
b.dtype  # dtype('int64')

taking numpy.exp() of any of these values will return a float64 :

numpy.exp(b[0, :]).dtype  # dtype('float64')

but if you now take this float64 and write it back into the original int64 array, it needs to be cast first:

b[0, :] = numpy.exp(b[0, :])
b.dtype  # dtype('int64')

Note that using

b = numpy.exp(b)

creates a new array with its own dtype . If instead you did

b[:] = numpy.exp(b[:])

you would be implicitly casting to int64 again.

Also note that there is no need to write a loop like you did. Instead you can vectorize the operation:

np.exp(matrix - numpy.max(matrix, axis=1, keepdims=True))
# array([[ 0.36787944,  1.        ],
#        [ 0.36787944,  1.        ]])