numpy

问题 From Numpy documentation: >>> a = np.arange(10)**3 >>> a array([ 0, 1, 8, 27, 64, 125, 216, 343, 512, 729]) >>> a[2] 8 >>> a[2:5] array([ 8, 27, 64]) >>> a[:6:2] = -1000 # equivalent to a[0:6:2] = -1000; from start to position 6, exclusive, set every 2nd element to -1000 >>> a array([-1000, 1, -1000, 27, -1000, 125, 216, 343, 512, 729]) >>> a[ : :-1] # reversed a array([ 729, 512, 343, 216, 125, -1000, 27, -1000, 1, -1000]) >>> for i in a: ... print(i**(1/3.)) ... nan 1.0 nan 3.0 nan 5.0 6.0

问题 From Numpy documentation: >>> a = np.arange(10)**3 >>> a array([ 0, 1, 8, 27, 64, 125, 216, 343, 512, 729]) >>> a[2] 8 >>> a[2:5] array([ 8, 27, 64]) >>> a[:6:2] = -1000 # equivalent to a[0:6:2] = -1000; from start to position 6, exclusive, set every 2nd element to -1000 >>> a array([-1000, 1, -1000, 27, -1000, 125, 216, 343, 512, 729]) >>> a[ : :-1] # reversed a array([ 729, 512, 343, 216, 125, -1000, 27, -1000, 1, -1000]) >>> for i in a: ... print(i**(1/3.)) ... nan 1.0 nan 3.0 nan 5.0 6.0

问题 I have a 3d numpy array. It's an array of 2d square images, all of the same size. my task is to block out a random square patch of the images (set all the pixel values to 0). I can figure out how to do it in the case where i just have 1 image as follows x = np.random.randint(image_width - size) y = np.random.randint(image_width - size) image[x:x + size, y:y + size] = 0 where size is the size of the blocked out area. I'm not sure how to efficiently do this technique to an array of 2d images,

问题 I have a 3d numpy array. It's an array of 2d square images, all of the same size. my task is to block out a random square patch of the images (set all the pixel values to 0). I can figure out how to do it in the case where i just have 1 image as follows x = np.random.randint(image_width - size) y = np.random.randint(image_width - size) image[x:x + size, y:y + size] = 0 where size is the size of the blocked out area. I'm not sure how to efficiently do this technique to an array of 2d images,

问题 I'm having issues with allocatable arrays in f2py . In the code below (stored in mymod.f90 ), I created two modules, vars and worker : vars stores and allocates the array b worker contains subroutines to work with this array from vars . The first worker -subroutine adds a scalar to b . This works as expected. The problem is with the next routine that should add a vector of matching first dimension to the array b . The implemented subroutine add_vector2 works, but needs the dimension of the

问题 I'm having issues with allocatable arrays in f2py . In the code below (stored in mymod.f90 ), I created two modules, vars and worker : vars stores and allocates the array b worker contains subroutines to work with this array from vars . The first worker -subroutine adds a scalar to b . This works as expected. The problem is with the next routine that should add a vector of matching first dimension to the array b . The implemented subroutine add_vector2 works, but needs the dimension of the

问题 When I tried this code, from math import exp import numpy as np w1=2 b1=0.5 b2=0.75 X=[[0, 1, 1, 1], [1, 1, 1, 1]] y=(np.dot(w1,X)-b1) tanh=np.vectorize((1-exp(-2*y))/(1+exp(-2*y))) y_out=1/(1+np.exp(-tanh))-b2 print(y_out) I got this error: TypeError: only size-1 arrays can be converted to Python scalars.. Where am I making a mistake? 回答1: In [269]: import math In [270]: w1=2 ...: b1=0.5 ...: b2=0.75 ...: X=[[0, 1, 1, 1], [1, 1, 1, 1]] ...: y=(np.dot(w1,X)-b1) In [271]: X Out[271]: [[0, 1, 1

问题 When I tried this code, from math import exp import numpy as np w1=2 b1=0.5 b2=0.75 X=[[0, 1, 1, 1], [1, 1, 1, 1]] y=(np.dot(w1,X)-b1) tanh=np.vectorize((1-exp(-2*y))/(1+exp(-2*y))) y_out=1/(1+np.exp(-tanh))-b2 print(y_out) I got this error: TypeError: only size-1 arrays can be converted to Python scalars.. Where am I making a mistake? 回答1: In [269]: import math In [270]: w1=2 ...: b1=0.5 ...: b2=0.75 ...: X=[[0, 1, 1, 1], [1, 1, 1, 1]] ...: y=(np.dot(w1,X)-b1) In [271]: X Out[271]: [[0, 1, 1

问题 I decided to try using OpenGL VBO in Python to improve FPS. I found code, that worked perfectly fine in Linux OS (Ubuntu), but when I tried launching in Windows OS, the code resulted in a message: "GLUT Display callback with (),{} failed: returning None module 'numpy' has no attribute 'float128'" So, I can't run the code specifically on Windows, but because I want to create a cross-platform application, I really need to solve this. I've done a lot of research and only found that numpy

问题 I want to make a list of N random INTEGER numbers whose sum is equal to M number. I have used numpy and dirichlet function in Python, but this generate double random number array, I would like to generate integer random number. import numpy as np np.random.dirichlet(np.ones(n))*m The solution can use other distribution the sense is resolve the problem. 回答1: The problem with using dirichlet for this is that it is a distribution over real numbers. It will yield a vector of numbers in the range