[LAB] Week 2_Problem 1_노영래

[surodoot]

Problem

Week 2_Problem 1

Source Code

from random import random 
from typing import Callable
import numpy as np

##                         Problem 1                          ##
##                                                            ##
##            Arbitary x_train, y_train are given.            ##
##   Suppose that x and y have linear correlation, y=wx+b.    ##
##     In function training(), you should return [w, b].      ##
##          In function predict(), you should return          ##
##            list y_test corresponding to x_test.            ##
##                  Made by @jangyoujin0917                   ##
##                                                            ##

# NOTE : Feel free to use torch.optim and tensor.
theta_best = []

def training(x_train : list[float], y_train : list[float]) -> list[float]: # DO NOT MODIFY FUNCTION NAME
    # Data normalization code (prevents overflow when calculating MSE, prevents underfitting)
    # Note that you need to convert [w, b] to the original scale before returning value
    # w = w * (y_max - y_min)
    # b = b * (y_max - y_min) + y_min
    y_min = min(y_train)
    y_max = max(y_train)
    normalize = lambda y : (y - y_min)/(y_max - y_min)

    x_m = np.c_[np.ones((len(x_train), 1)), x_train]

    theta_best = np.linalg.inv(x_m.T.dot(x_m)).dot(x_m.T).dot(y_train)
    ### IMPLEMENT FROM HERE

    return theta_best.ravel()

def predict(x_train : list[float], y_train : list[float], x_test : list[float]) -> list[float]: # DO NOT MODIFY FUNCTION NAME
    x_m = np.c_[np.ones((len(x_train), 1)), x_train]

    theta_best = np.linalg.inv(x_m.T.dot(x_m)).dot(x_m.T).dot(y_train)
    x = np.c_[np.ones((len(x_test), 1)), x_test]
    y_predict = x.dot(theta_best)

    return y_predict

if __name__ == "__main__":
    x_train = [0.0, 1.0, 2.0, 3.0, 4.0]
    y_train = [2.0, 4.0, 6.0, 8.0, 10.0] # Note that not all test cases give clear line.
    x_test = [5.0, 10.0, 8.0]

    w, b = training(x_train, y_train)
    y_test = predict(x_train, y_train, x_test)

    print(w, b)
    print(y_test)

Description

Matrix product

Output (Optional)

No response

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This is an auto-generated grading output. Checking code of surodoot {'surodoot': 54.1}

[Dongyeongkim]

This issue is closed now because of the lack of progress.