dangerous_slope.py

import numpy as np
import math


def sigmoid(x):
    return 1 / (1 + math.exp(-x))


def hypothesis(weights, x):
    h = np.dot(weights, x)
    return sigmoid(h)


def decision(probability):
    if probability >= 0.5:
        return "dangerous"
    return "safe"


def cost_derivative(weights, x, y, feature_x):
    s = 0
    m = len(x[0])
    for i in range(m):
        s += (hypothesis(weights, x[:, i]) - y[i]) * feature_x[i]
    return s / m


def train(x, y, a, number_of_iterations):
    i = 0
    n = len(x)  # number of features
    weights = [0] * n

    while i < number_of_iterations:
        temp_weights = [0] * n
        for j in range(n):
            temp_weights[j] = weights[j] - a * cost_derivative(weights, x, y, x[j])

        if temp_weights == weights:
            return weights

        for j in range(n):
            weights[j] = temp_weights[j]
        i += 1

    return weights


# slope degrees: for ex. 27° converted to 0.27 to avoid overflow
slopes = [0.27, 0.21, 0.38, 0.18, 0.7, 0.68, 0.36, 0.49, 0.27, 0.5,
          0.47, 0.62, 0.44, 0.57, 0.55, 0.36, 0.69, 0.65, 0.67, 0.3,
          0.63, 0.41, 0.33, 0.23, 0.22, 0.48, 0.52, 0.36, 0.52, 0.33,
          0.18, 0.29, 0.43, 0.62, 0.35, 0.34, 0.27, 0.43, 0.54, 0.21]

# average precipitation: for ex. 60% converted to 0.60
precis = [0.6, 0.56, 0.26, 0.56, 0.1, 0.11, 0.05, 0.34, 0.55, 0.06,
          0.58, 0.51, 0.46, 0.02, 0.44, 0.53, 0.24, 0.42, 0.4, 0.2,
          0.47, 0.48, 0.2, 0.01, 0.49, 0.13, 0.25, 0.31, 0.1, 0.51,
          0.48, 0.51, 0.19, 0.42, 0.28, 0.15, 0.4, 0.6, 0.56, 0.43]

# probability of the maintenance: for ex. 45% converted to 0.45
output = [0.54, 0.49, 0.35, 0.48, 0.35, 0.35, 0.2, 0.44, 0.51, 0.26,
          0.59, 0.6, 0.5, 0.25, 0.53, 0.52, 0.44, 0.55, 0.54, 0.28,
          0.57, 0.51, 0.29, 0.13, 0.45, 0.3, 0.39, 0.38, 0.29, 0.5,
          0.43, 0.49, 0.32, 0.54, 0.35, 0.26, 0.41, 0.59, 0.6, 0.41]

bias = []
for i in range(len(slopes)):
    bias.append(1)

x = np.array([bias, slopes, precis])
w = train(x, output, 0.7, 1500)
print("weights = " + str(w))

prob = hypothesis(w, [1, 0.22, 0.50])
print("\nTest for a slope with 22° and precipitation of 50%:")
print("Probability: " + str(prob))  # ~0.454
print("This cut slope is " + decision(prob))  # safe

prob = hypothesis(w, [1, 0.42, 0.60])
print("\nTest for a slope with 52° and precipitation of 60%:")
print("Probability: " + str(prob))  # ~0.594
print("This cut slope is " + decision(prob))  # dangerous