numpy

问题 I load images with numpy/scikit. I know that all images are 200x200 pixels. When the images are loaded, I notice some have an alpha channel, and therefore have shape (200, 200, 4) instead of (200, 200, 3) which I expect. Is there a way to delete that last value, discarding the alpha channel and get all images to a nice (200, 200, 3) shape? 回答1: Just slice the array to get the first three entries of the last dimension: image_without_alpha = image[:,:,:3] 来源： https://stackoverflow.com/questions

问题 I use simple polyfit of order 2 to fit a line in sample data: np.polyfit(x, y, 2) which returns the coefficients. Now I want to find uncertainty of the fitted line, and tried to use cov argument, which returns 3x3 covariance matrix: np.polyfit(x, y, 2, cov=True) But I'm not sure how to calculate the uncertainty, which according my Google search should be calculated by squaring the diagonal of covariance matrix. 回答1: This problem is addressed by "Estimating Errors in Least-Squares Fitting" by

问题 I have developed Bicubic interpolation for demonstration to some undergraduate students using Python Programming language. The methodology is as explained in wikipedia, The code is working fine except the results I am getting are slightly different than what is obtained when using scipy library. The interpolation code is shown below in the function bicubic_interpolation . import numpy as np import matplotlib.pyplot as plt from mpl_toolkits import mplot3d from scipy import interpolate import

问题 While I have found numerous answers to questions similar to mine, I don't believe it has been directly addressed here--and I have several additional questions. The motivation for sharing contiguous numpy arrays is as follows: I'm using a convolutional neural network run on Caffe to perform a regression on images to a series of continuous-value labels. The images require specific preprocessing and data augmentation. The constraints of (1) the continuous nature of the labels (they're floats)

问题 While I have found numerous answers to questions similar to mine, I don't believe it has been directly addressed here--and I have several additional questions. The motivation for sharing contiguous numpy arrays is as follows: I'm using a convolutional neural network run on Caffe to perform a regression on images to a series of continuous-value labels. The images require specific preprocessing and data augmentation. The constraints of (1) the continuous nature of the labels (they're floats)

问题 During my years on Python development, I've always been amazed at how much much much faster things become if you manage to rewrite that code that loops though your ndarray and does something, with numpy functions that work on the whole array at once. More recently I'm switching more and more to node, and I'm looking for something similar. So far I have turned up some things, none of which look promising: scikit-node, runs scikit-learn in python, and interfaces with node. I haven't tried it,

问题 During my years on Python development, I've always been amazed at how much much much faster things become if you manage to rewrite that code that loops though your ndarray and does something, with numpy functions that work on the whole array at once. More recently I'm switching more and more to node, and I'm looking for something similar. So far I have turned up some things, none of which look promising: scikit-node, runs scikit-learn in python, and interfaces with node. I haven't tried it,

问题 I want to create a list of points that would correspond to a grid. So if I want to create a grid of the region from (0, 0) to (1, 1) , it would contain the points (0, 0) , (0, 1) , (1, 0) and (1, 0) . I know that that this can be done with the following code: g = np.meshgrid([0,1],[0,1]) np.append(g[0].reshape(-1,1),g[1].reshape(-1,1),axis=1) Yielding the result: array([[0, 0], [1, 0], [0, 1], [1, 1]]) My question is twofold: Is there a better way of doing this? Is there a way of generalizing

问题 I want to create a list of points that would correspond to a grid. So if I want to create a grid of the region from (0, 0) to (1, 1) , it would contain the points (0, 0) , (0, 1) , (1, 0) and (1, 0) . I know that that this can be done with the following code: g = np.meshgrid([0,1],[0,1]) np.append(g[0].reshape(-1,1),g[1].reshape(-1,1),axis=1) Yielding the result: array([[0, 0], [1, 0], [0, 1], [1, 1]]) My question is twofold: Is there a better way of doing this? Is there a way of generalizing

问题 I have the following dataframe: Month 1 -0.075844 2 -0.089111 3 0.042705 4 0.002147 5 -0.010528 6 0.109443 7 0.198334 8 0.209830 9 0.075139 10 -0.062405 11 -0.211774 12 -0.109167 1 -0.075844 2 -0.089111 3 0.042705 4 0.002147 5 -0.010528 6 0.109443 7 0.198334 8 0.209830 9 0.075139 10 -0.062405 11 -0.211774 12 -0.109167 Name: Passengers, dtype: float64 As you can see numbers are listed twice from 1-12 / 1-12, instead, I would like to change the index to 1-24. The problem is that when plotting