import numpy as np
import csv
import sys

from validate import validate

"""
Predicts the target values for data in the file at 'test_X_file_path', using the weights learned during training.
Writes the predicted values to the file named "predicted_test_Y_lr.csv". It should be created in the same directory where this code file is present.
This code is provided to help you get started and is NOT a complete implementation. Modify it based on the requirements of the project.
"""

def import_data_and_weights(test_X_file_path):
    test_X = np.genfromtxt(test_X_file_path, delimiter=',', dtype=np.float64, skip_header=1)
    return test_X


def predict_target_values(test_X):
    # Write your code to Predict Target Variables
    # HINT: You can use other functions which you've already implemented in coding assignments.
    Xi = test_X
    test_X = np.ones((len(Xi), 5))
    test_X[:, 1:] = Xi
    W = np.array([-3676.390363578219,0.8089489732182642,68.00621804893717,42.40994137279017,28.47903249472438])
    pred_Y = np.matmul(test_X, W)
    return pred_Y


def write_to_csv_file(pred_Y, predicted_Y_file_name):
    pred_Y = pred_Y.reshape(len(pred_Y), 1)
    with open(predicted_Y_file_name, 'w', newline='') as csv_file:
        wr = csv.writer(csv_file)
        wr.writerows(pred_Y)
        csv_file.close()


def predict(test_X_file_path):
    test_X = import_data_and_weights(test_X_file_path)
    pred_Y = predict_target_values(test_X)
    write_to_csv_file(pred_Y, "predicted_test_Y_lr.csv")


if __name__ == "__main__":
    test_X_file_path = sys.argv[1]
    predict(test_X_file_path)
    # Uncomment to test on the training data
    # validate(test_X_file_path, actual_test_Y_file_path="train_Y_lr.csv") 
c57e39bec767e10570c4f7a7f0032e6b8e0dfebe
<mask token>


def import_data_and_weights(test_X_file_path):
    test_X = np.genfromtxt(test_X_file_path, delimiter=',', dtype=np.
        float64, skip_header=1)
    return test_X


def predict_target_values(test_X):
    Xi = test_X
    test_X = np.ones((len(Xi), 5))
    test_X[:, 1:] = Xi
    W = np.array([-3676.390363578219, 0.8089489732182642, 68.00621804893717,
        42.40994137279017, 28.47903249472438])
    pred_Y = np.matmul(test_X, W)
    return pred_Y


def write_to_csv_file(pred_Y, predicted_Y_file_name):
    pred_Y = pred_Y.reshape(len(pred_Y), 1)
    with open(predicted_Y_file_name, 'w', newline='') as csv_file:
        wr = csv.writer(csv_file)
        wr.writerows(pred_Y)
        csv_file.close()


<mask token>

<mask token>


def import_data_and_weights(test_X_file_path):
    test_X = np.genfromtxt(test_X_file_path, delimiter=',', dtype=np.
        float64, skip_header=1)
    return test_X


def predict_target_values(test_X):
    Xi = test_X
    test_X = np.ones((len(Xi), 5))
    test_X[:, 1:] = Xi
    W = np.array([-3676.390363578219, 0.8089489732182642, 68.00621804893717,
        42.40994137279017, 28.47903249472438])
    pred_Y = np.matmul(test_X, W)
    return pred_Y


def write_to_csv_file(pred_Y, predicted_Y_file_name):
    pred_Y = pred_Y.reshape(len(pred_Y), 1)
    with open(predicted_Y_file_name, 'w', newline='') as csv_file:
        wr = csv.writer(csv_file)
        wr.writerows(pred_Y)
        csv_file.close()


def predict(test_X_file_path):
    test_X = import_data_and_weights(test_X_file_path)
    pred_Y = predict_target_values(test_X)
    write_to_csv_file(pred_Y, 'predicted_test_Y_lr.csv')


<mask token>

<mask token>


def import_data_and_weights(test_X_file_path):
    test_X = np.genfromtxt(test_X_file_path, delimiter=',', dtype=np.
        float64, skip_header=1)
    return test_X


def predict_target_values(test_X):
    Xi = test_X
    test_X = np.ones((len(Xi), 5))
    test_X[:, 1:] = Xi
    W = np.array([-3676.390363578219, 0.8089489732182642, 68.00621804893717,
        42.40994137279017, 28.47903249472438])
    pred_Y = np.matmul(test_X, W)
    return pred_Y


def write_to_csv_file(pred_Y, predicted_Y_file_name):
    pred_Y = pred_Y.reshape(len(pred_Y), 1)
    with open(predicted_Y_file_name, 'w', newline='') as csv_file:
        wr = csv.writer(csv_file)
        wr.writerows(pred_Y)
        csv_file.close()


def predict(test_X_file_path):
    test_X = import_data_and_weights(test_X_file_path)
    pred_Y = predict_target_values(test_X)
    write_to_csv_file(pred_Y, 'predicted_test_Y_lr.csv')


if __name__ == '__main__':
    test_X_file_path = sys.argv[1]
    predict(test_X_file_path)

import numpy as np
import csv
import sys
from validate import validate
<mask token>


def import_data_and_weights(test_X_file_path):
    test_X = np.genfromtxt(test_X_file_path, delimiter=',', dtype=np.
        float64, skip_header=1)
    return test_X


def predict_target_values(test_X):
    Xi = test_X
    test_X = np.ones((len(Xi), 5))
    test_X[:, 1:] = Xi
    W = np.array([-3676.390363578219, 0.8089489732182642, 68.00621804893717,
        42.40994137279017, 28.47903249472438])
    pred_Y = np.matmul(test_X, W)
    return pred_Y


def write_to_csv_file(pred_Y, predicted_Y_file_name):
    pred_Y = pred_Y.reshape(len(pred_Y), 1)
    with open(predicted_Y_file_name, 'w', newline='') as csv_file:
        wr = csv.writer(csv_file)
        wr.writerows(pred_Y)
        csv_file.close()


def predict(test_X_file_path):
    test_X = import_data_and_weights(test_X_file_path)
    pred_Y = predict_target_values(test_X)
    write_to_csv_file(pred_Y, 'predicted_test_Y_lr.csv')


if __name__ == '__main__':
    test_X_file_path = sys.argv[1]
    predict(test_X_file_path)

import numpy as np
import csv
import sys

from validate import validate

"""
Predicts the target values for data in the file at 'test_X_file_path', using the weights learned during training.
Writes the predicted values to the file named "predicted_test_Y_lr.csv". It should be created in the same directory where this code file is present.
This code is provided to help you get started and is NOT a complete implementation. Modify it based on the requirements of the project.
"""

def import_data_and_weights(test_X_file_path):
    test_X = np.genfromtxt(test_X_file_path, delimiter=',', dtype=np.float64, skip_header=1)
    return test_X


def predict_target_values(test_X):
    # Write your code to Predict Target Variables
    # HINT: You can use other functions which you've already implemented in coding assignments.
    Xi = test_X
    test_X = np.ones((len(Xi), 5))
    test_X[:, 1:] = Xi
    W = np.array([-3676.390363578219,0.8089489732182642,68.00621804893717,42.40994137279017,28.47903249472438])
    pred_Y = np.matmul(test_X, W)
    return pred_Y


def write_to_csv_file(pred_Y, predicted_Y_file_name):
    pred_Y = pred_Y.reshape(len(pred_Y), 1)
    with open(predicted_Y_file_name, 'w', newline='') as csv_file:
        wr = csv.writer(csv_file)
        wr.writerows(pred_Y)
        csv_file.close()


def predict(test_X_file_path):
    test_X = import_data_and_weights(test_X_file_path)
    pred_Y = predict_target_values(test_X)
    write_to_csv_file(pred_Y, "predicted_test_Y_lr.csv")


if __name__ == "__main__":
    test_X_file_path = sys.argv[1]
    predict(test_X_file_path)
    # Uncomment to test on the training data
    # validate(test_X_file_path, actual_test_Y_file_path="train_Y_lr.csv") 