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Given a 2D array, feature matrix X and a vector y, return the coefficient vector, see the formula.

Task Given a 2D array, feature matrix X and a vector y, return the coefficient vector, see the formula. Input Format First line: two integers separated by spaces, the first indicates the rows of matrix X (n) and the second indicates the columns of X (p) Next n lines: values of the row in the feature matrix Last line: p values of target y Output Format An numpy 1d array of values rounded to the second decimal. 2 2 1 0 0 2 Sample Output [2. 1.5]

12th Nov 2021, 7:23 PM
NaBeeL NaQeeBi
NaBeeL NaQeeBi - avatar
5 odpowiedzi
+ 1
import numpy as np from numpy.linalg import inv n, p = [int(x) for x in input().split()] X = [] for i in range(n): X.append([float(x) for x in input().split()]) y = [float(x) for x in input().split()] arX = np.array(X) ary = np.array(y) # Gram Matrix = X.T * X or <Transpose of matrix regressor variable X>*<matrix regressor variable X> gramInverse = inv(np.dot(arX.T, arX)) # Moment Matrix = X.T * y or <Transpose of matrix regressor variable X> * <vector of the value of the response variable> moment = np.dot(arX.T, ary) # beta = inverse of (Gram Matrix) * Moment Matrix beta = np.dot(gramInverse, moment) print(np.around(beta, decimals = 2))
24th Apr 2022, 6:09 PM
Sifar
+ 1
# I found this code works import numpy as np n, p = [int(x) for x in input().split()] X = [] for i in range(n): X.append([float(x) for x in input().split()]) y = [float(x) for x in input().split()] X=np.linalg.lstsq(X, y, rcond = -1) print (X[0].round(2))
26th Oct 2022, 8:52 AM
Adil Omar
Adil Omar - avatar
0
I've written a code which is as follows import numpy as np n, p = [int(x) for x in input().split()] lista = [] for i in range(n): lista.append(input().split()) print(np.array(lista).astype(np.float16).mean(axis=1).round(2)) [input] 2 2 1 0 0 2 [output] [0.5 1. ] required output [2., 1.5]
12th Nov 2021, 7:26 PM
NaBeeL NaQeeBi
NaBeeL NaQeeBi - avatar
0
n, p = [int(x) for x in input().split()] X = [] for i in range(n): X.append([float(x) for x in input().split()]) y = [float(x) for x in input().split()] import numpy as np coef_mat = np.linalg.lstsq(X ,y, rcond= None)[0].round(2) print(coef_mat)
17th Feb 2022, 7:55 AM
Arshed Abdel Rhman Mohammed Basheer
Arshed Abdel Rhman Mohammed Basheer - avatar
0
import numpy as np X = np.array(X) y = np.array(y) solucion = np.linalg.lstsq(X, y, rcond=-1) print (solucion[0].round(2))
13th Nov 2022, 9:13 PM
Jeanfreddy Gutiérrez
Jeanfreddy Gutiérrez - avatar