numpy-broadcasting

The term broadcasting describes how numpy treats arrays with different shapes during arithmetic operations. Example 1: from numpy import array a = array([1.0,2.0,3.0]) b = array([2.0,2.0,2.0]) # multiply element-by-element () a * b >> array([ 2., 4., 6.]) Example 2 : from numpy import array a = array([1.0,2.0,3.0]) b = 2.0 # broadcast b to all a a * b >>array([ 2., 4., 6.]) We can think of the scalar b being stretched during the arithmetic operation into an array with the same shape as a. Numpy is smart enough to use the original scalar value without actually making copies so that broadcasting

This question is a follow up to my answer in Efficient way to compute the Vandermonde matrix . Here's the setup: x = np.arange(5000) # an integer array N = 4 Now, I'll compute the Vandermonde matrix in two different ways: m1 = (x ** np.arange(N)[:, None]).T And, m2 = x[:, None] ** np.arange(N) Sanity check: np.array_equal(m1, m2) True These methods are identical, but their performance is not: %timeit m1 = (x ** np.arange(N)[:, None]).T 42.7 µs ± 271 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each) %timeit m2 = x[:, None] ** np.arange(N) 150 µs ± 995 ns per loop (mean ± std. dev. of 7

Broadcasting is the process of making arrays with different shapes have compatible shapes for arithmetic operations. In numpy, we can broadcast arrays. Does TensorFlow graph support broadcasting similar to the numpy one? yes it is supported. Open a terminal and try this: import tensorflow as tf #define tensors a=tf.constant([[10,20],[30,40]]) #Dimension 2X2 b=tf.constant([5]) c=tf.constant([2,2]) d=tf.constant([[3],[3]]) sess=tf.Session() #start a session #Run tensors to generate arrays mat,scalar,one_d,two_d = sess.run([a,b,c,d]) #broadcast multiplication with scalar sess.run(tf.multiply(mat

I want to subtract a column vector from a numpy matrix using another vector which is index of columns where the first column vector needs to be subtracted from the main matrix. For eg. M = array([[ 1, 2, 1, 1], [ 2, 1, 1, 1], [ 1, 1, 2, 1], [ 2, 1, 1, 1], [ 1, 1, 1, 2]]) # An example matrix V = array([1, 1, 1, 1, 1]) # An example column vector I = array([0, 3, 2, 3, 1, 3, 3]) # The index maxtrix Now I want to subtract V from M at column numbers given in I. For eg. I[0] is 0, so subtract V from first column (zero index) of matrix M. Similarly I[1] = 3, subtract V from fourth column (three index

Let's say I have an array like this: import numpy as np base_array = np.array([-13, -9, -11, -3, -3, -4, 2, 2, 2, 5, 7, 7, 8, 7, 12, 11]) Suppose I want to know: "how many elements in base_array are greater than 4?" This can be done simply by exploiting broadcasting: np.sum(4 < base_array) For which the answer is 7 . Now, suppose instead of comparing to a single value, I want to do this over an array. In other words, for each value c in the comparison_array , find out how many elements of base_array are greater than c . If I do this the naive way, it obviously fails because it doesn't know how