Can someone explain these two formulas? Do they have any relationship? def _cosine_distance(a, b, data_is_normalized=False): if not data_is_normalized:
I'm having trouble with the formatting of my array when it it saved to a csv file, each time I run the program I get a different result, sometimes the string "c
My current code functions and produces a graph if there is only 1 sensor, i.e. if col2, and col3 are deleted in the example data provided below, leaving one col
Trying to understand what kind of output do I get from the histogram function. phase = mod(phase,Nper*2*pi) cl_phase = arange(0,Nper*2*pi+step,step) c,p = histo
import numpy as np import math as mt import matplotlib.pyplot as plt import mpl_toolkits.mplot3d.axes3d as axes3d #############################################
I have a data frame that has 3 columns and I want to plot a line graph based on some thresholds. Here is the data frame date income ratio 0 2022-0
Let's say I have a numpy array my_array = [0.2, 0.3, nan, nan, nan, 0.1, nan, 0.5, nan] For each nan value, I want to extract the two non-nan values to the lef
I am setting up a tunnel with gcloud from a remote machine with: gcloud compute start-iap-tunnel ... Everything used to be fine until a few days ago when it st
I want to try and recreate this functions from scratch (without using sklearn): # The matrix is M which is 1000x10 matrix. from sklearn.preprocessing import Po
Let's say that I have an array like this: array([[ 1, 2], [-1, -2], [ 0, 0], [-1, 2], [ 2, -1]]) I want to filter out all rows t
Let's say I have two array, one with all zero elements and the second one with indices that should be filled with 1. This can be done with the following code: A
Please assume the following NumPy array: A = array([1, 1, 0, 1, 0, 0, 0, 0, 0, 0]) I would like to find the indices of this array that N consecutive values are
trying to save an inverted image, saved inverted RGB colour data in array pixelArray, then converted this to a numpy array. Not sure what is wrong but any help
I have a pandas dataframe and a numpy array of values of that dataframe. I have the index of a specific column and I already have the row index of an important
I need to implement xor between floats in python for huge 2D array data (like thousand row by thousand column matrix). I use the following implementation: impor
As stated in the title, I want to obtain the n-th (e.g. 4-th) order antiderivative of a 3D field (e.g. array with shape (1024,1024,1024) ) with period L on each
Can someone explain exactly what the axis parameter in NumPy does? I am terribly confused. I'm trying to use the function myArray.sum(axis=num) At first I t
I'm trying to make an AI attempting to predict numbers from prime number sequence, but my model outputs "[[nan]]". My csv file is formatted like this: number of
Background My sentiment analysis research comes across a variety of datasets. Recently I've encountered one dataset that somehow I just cannot train successfull
import cv2 damaged_image = cv2.imread("Corrupted.png") mask = cv2.imread("mask.png", 0) output = cv2.inpaint(damaged_image, mask, 1, cv2.INPAINT_TELEA) cv2.i