numpy-ndarray

问题 I have a 5*6 matrix, all of the values are the English alphabets. I have to find specific words in the matrix in left to right, right to left, up down, down up and diagonally. It's a word puzzle actually. I can find the words left right, right left, up down and down up. When it comes to finding the words diagonally things get messier. I have provided the sample code for the left to right and right to left search. #the word I am looking for word_list = ["CRAM", "ROTLQ", "TDML", "COOI"] #

问题 I have found myself needing to add features to existing numpy arrays which has led to a question around what the last portion of the following code is actually doing: np.ones(shape=feature_set.shape)[...,None] Set-up As an example, let's say I wish to solve for linear regression parameter estimates by using numpy and solving: Assume I have a feature set shape (50,1), a target variable of shape (50,), and I wish to use the shape of my target variable to add a column for intercept values. It

问题 I have two sets of coordinates and want to find out which coordinates of the coo set are identical to any coordinate in the targets set. I want to know the indices in the coo set which means I'd like to get a list of indices or of bools. import numpy as np coo = np.array([[1,2],[1,6],[5,3],[3,6]]) # coordinates targets = np.array([[5,3],[1,6]]) # coordinates of targets print(np.isin(coo,targets)) [[ True False] [ True True] [ True True] [ True True]] The desired result would be one of the

问题 I have two sets of coordinates and want to find out which coordinates of the coo set are identical to any coordinate in the targets set. I want to know the indices in the coo set which means I'd like to get a list of indices or of bools. import numpy as np coo = np.array([[1,2],[1,6],[5,3],[3,6]]) # coordinates targets = np.array([[5,3],[1,6]]) # coordinates of targets print(np.isin(coo,targets)) [[ True False] [ True True] [ True True] [ True True]] The desired result would be one of the

问题 I have two sets of coordinates and want to find out which coordinates of the coo set are identical to any coordinate in the targets set. I want to know the indices in the coo set which means I'd like to get a list of indices or of bools. import numpy as np coo = np.array([[1,2],[1,6],[5,3],[3,6]]) # coordinates targets = np.array([[5,3],[1,6]]) # coordinates of targets print(np.isin(coo,targets)) [[ True False] [ True True] [ True True] [ True True]] The desired result would be one of the

问题 Consider an image img of type imageio.core.util.Array . The shape of img is (256, 256, 3) . I want to resize it to (128, 128, 3) . I tried at least the following three: img.resize(img_res, pilmode="RGB") img.resize(img_res) img = cv2.resize(img, self.img_res) Here img_res = (128, 128) . None of them worked well. How can I resize my image to the desired size? 回答1: According to the documentation on imageio.core.util.Array, Array is "a subclass of np.ndarray [...]". Thus, when calling resize on

问题 I find myself doing the following quite frequently and am wondering if there's a "canonical" way of doing it. I have an ndarray say shape = (100, 4, 6) and I want to reduce to (100, 24) by concatenating the 4 vectors of length 6 into one vector I can use reshape to do this but I've been manually computing the new shape i.e. np.reshape(x,shape=(a.shape[0],a.shape[1]*a.shape[2])) ideally I'd simply supply the dimension I want to reduce on np.concatenate(x,dim=-1) but np.concatenate operates on

问题 For a 2D array like this: table = np.array([[11,12,13],[21,22,23],[31,32,33],[41,42,43]]) Is it possible to use np.reshape on table to get an array single_column where each column of table is stacked vertically? This can be accomplished by splitting table and combining with vstack . single_column = np.vstack(np.hsplit(table , table .shape[1])) Reshape can combine all the rows into a single row, I'm wondering if it can combine the columns as well to make the code cleaner and possibly faster.

问题 For a 2D array like this: table = np.array([[11,12,13],[21,22,23],[31,32,33],[41,42,43]]) Is it possible to use np.reshape on table to get an array single_column where each column of table is stacked vertically? This can be accomplished by splitting table and combining with vstack . single_column = np.vstack(np.hsplit(table , table .shape[1])) Reshape can combine all the rows into a single row, I'm wondering if it can combine the columns as well to make the code cleaner and possibly faster.

问题 I'm optimising my implementation of the back-propagation algorithm to train a neural network. One of the aspects I'm working on is performing the matrix operations on the set of datapoints (input/output vector) as a batch process optimised by the numpy library instead of looping through every datapoint. In my original algorithm I did the following: for datapoint in datapoints: A = ... (created out of datapoint info) B = ... (created out of datapoint info) C = np.dot(A,B.transpose()) _________