Imagine predicting BMI index like 1,2,3,4,5 and having weight and height as input. I know it can be easily done with other method. Also I have to use sigmoid function. and I am really new to this. I can't seem to find the solution to this question.
As much as I know so far, perceptron is only linearly separable and sigmoid function gives value between 0 and 1. I can't figure out the way to classify the index value 1,2,3,4,5 which are BMI index.
- No perceptron or any library except numpy and pandas
- Use only sigmoid function.
import pandas as pd
import numpy as np
df = pd.read_csv('bmi.csv')
X = df[['Height', 'Weight']].values
y = df['Index'].values / 5.0 # Normalize targets
#
X = (X - np.mean(X, axis=0)) / np.std(X, axis=0)
X = np.c_[X, np.ones(X.shape[0])]
np.random.seed(42)
weights = np.random.uniform(low=-0.5, high=0.5, size=X.shape[1])
learning_rate = 0.1
num_iterations = 10000
for _ in range(num_iterations):
for i in range(X.shape[0]):
# Forward pass
weighted_sum = np.dot(X[i], weights)
output = weighted_sum
error = y[i] - output
weights += learning_rate * error * X[i]
test_input = np.array([174, 96])
test_input = (test_input - np.mean(X[:, :2], axis=0)) / np.std(X[:, :2], axis=0)
test_input = np.append(test_input, 1)
predicted_output = np.dot(test_input, weights)
predicted_bmi = predicted_output * 5.0
print("Predicted BMI:", predicted_bmi)
```