My first ever machine learning with 100% code by me. No ML helper libraries used.

machine-learning-linear-regression.py

import math                     # this library helps with mathematics
import matplotlib.pyplot as plt # this library helps visual plotting

# our data points in a 2D matrix
data = [
    [0.5, 1.4],
    [2.3, 1.9],
    [2.9, 3.2],
]

# initalize profiling weights
lossFormattedY = []

# intialize plotter
fig, (ax1, ax2) = plt.subplots(2)
fig.suptitle('by Chaidhat Chaimongkol')


# initalize our weights
# note: slope = w1*x + w0 (y = mx + c)
w0 = 1                  # slope intercept (m)
w1 = 1                  # y-intercept (c)

learningRate = 0.01     # how fast to train the AI
maxEpochs = 500         # maximum amount of epochs


for epoch in range(maxEpochs):                          # for each epoch

    # initalize our variables
    loss = 0
    dW0 = 0
    dW1 = 0
    for dataPoint in data:                              # for each datapoint in data
        dataPointX = dataPoint[0]                       # x coordinate of point

        # linear regression (w1*x + w0) (y = mx + c)
        predictedValue = w1 * dataPointX + w0           # our predicted value
        actualValue = dataPoint[1]                      # the actual y coordinate of point

        # error calculation
        error = actualValue - predictedValue

        # loss calculation (Mean Squared Error)
        loss += (error) ** 2                            # loss = SUM OF (actual - predicted) ^ 2
        # derivative of loss w.r.t w0
        dW0 += -2 * (error)                             # -2 * error
        dW1 += -2 * dataPointX * (error)                # -2 * x * error

    #loss = loss / len(data[0])
    # now flip the gradient vector to point to the local minima
    dW0 *= -1
    dW1 *= -1

    print("epoch", epoch)
    print("loss", loss)

    print("dW0", dW0)
    print("dW1", dW1)

    w0 += dW0 * learningRate
    w1 += dW1 * learningRate

    # plot data
    # format the data for matplotlib
    dataFormattedX = [i[0] for i in data]   # get all the x coords of data
    dataFormattedY = [i[1] for i in data]   # get all the y coords of data
    # plot the data
    ax1.plot(dataFormattedX, dataFormattedY, 'ro')
    # plot the line of best fit from x = 0 to maximum x
    ax1.plot([0, max(dataFormattedX)], [w0, w1 * max(dataFormattedY) + w0], "b-")
    plt.pause(0.05)

    # plot profile
    lossFormattedX = list(range(1,epoch + 2)) # generate 1-epoch numbers
    lossFormattedY.append(loss)
    ax2.plot(lossFormattedX, lossFormattedY, "r-")

plt.show()