linear_reg.py

import numpy as np

def linear_predictions(weights, inputs):
    # y = weights[0] inputs[0] + weights[1] * inputs[1]
    # where inputs[0] = 1.0
    return np.dot(inputs, weights) * 60.0

def squared_loss(weights, inputs, targets):
    # Training loss is the negative squared loss
    preds = linear_predictions(weights, inputs)
    err = (preds - targets)**2
    return np.sum(err)

v_avg = 30 # km/h
startup_time = 2 /60.0 # hours

inputs = np.array([[1.0, 6.0],
                   [1.0, 4.0 ]])
targets = np.array([13, 10.5])

weights = np.array([startup_time, 1.0 / v_avg]) # Program params are estimaed by experience, stats analysis, Least Square Error, ...
print("Trained loss:", squared_loss(weights, inputs, targets))