Min-max scaling transforms every feature column independently into the range from 0 through 1. For a value in row i and column j, compute
xij′=maxj−minjxij−minjThe numerator subtracts the minimum value of column j, and the denominator is that column's maximum minus its minimum. If a column is constant, the denominator is zero; map every value in that column to 0.0. Return a floating-point matrix with the same shape as data.
Input: data = [[1, 10], [2, 20], [3, 30]]
Output: [[0.0, 0.0], [0.5, 0.5], [1.0, 1.0]]
Explanation: Each column is scaled using its own minimum and maximum.
Input: data = [[0, 0], [10, 100], [20, 50]]
Output: [[0.0, 0.0], [0.5, 1.0], [1.0, 0.5]]
Collect the minimum and maximum of one column before scaling its entries.
Initialize an output matrix with the same row and column counts as data.
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Accepts: array
Min-max scaling transforms every feature column independently into the range from 0 through 1. For a value in row i and column j, compute
xij′=maxj−minjxij−minjThe numerator subtracts the minimum value of column j, and the denominator is that column's maximum minus its minimum. If a column is constant, the denominator is zero; map every value in that column to 0.0. Return a floating-point matrix with the same shape as data.
Input: data = [[1, 10], [2, 20], [3, 30]]
Output: [[0.0, 0.0], [0.5, 0.5], [1.0, 1.0]]
Explanation: Each column is scaled using its own minimum and maximum.
Input: data = [[0, 0], [10, 100], [20, 50]]
Output: [[0.0, 0.0], [0.5, 1.0], [1.0, 0.5]]
Collect the minimum and maximum of one column before scaling its entries.
Initialize an output matrix with the same row and column counts as data.
Sign in to take notes on this problem
Accepts: array