Q4 fin

30/30

Q4.py

@@ -0,0 +1,105 @@
+import numpy as np
+from csv import reader
+from random import seed
+from random import randrange
+
+def load_csv(filename, skip=False):
+    dataset = list()  # Keep the starter code structure intact
+    with open(filename, 'r', newline='') as file:
+        csv_reader = reader(file)
+        if skip:
+            next(csv_reader)  # Skip the header row if needed
+        for row in csv_reader:
+            if not row:
+                continue
+            dataset.append([float(value) for value in row])  # Convert all values to float
+    return dataset
+
+
+# Split the dataset into X_train, Y_train, X_test, Y_test sets.
+
+def train_test_split(dataset, split):
+    train_size = int(split * len(dataset))
+    train_set = dataset[:train_size]
+    test_set = dataset[train_size:]
+
+    X_train = [row[:-1] for row in train_set]
+    y_train = [row[-1] for row in train_set]
+    X_test = [row[:-1] for row in test_set]
+    y_test = [row[-1] for row in test_set]
+
+    return X_train, y_train, X_test, y_test
+
+
+#Defining the Perceptron class that contains the weights, bias, learning rate and epochs.
+
+class Perceptron:
+    def __init__(self, input_size, bias, learning_rate, epochs):
+        self.weights = np.zeros(input_size)
+        self.bias = bias
+        self.learning_rate = learning_rate
+        self.epochs = epochs
+
+def activation_function(x):
+    return 1 if x >= 0 else 0
+
+
+
+def predict(inputs, weights, bias):
+    weighted_sum = np.dot(inputs, weights) + bias
+    return activation_function(weighted_sum)
+
+
+def train(X_train, y_train, learning_rate, epochs, weights, bias):
+    for epoch in range(epochs):
+        for inputs, target in zip(X_train, y_train):
+            prediction = predict(inputs, weights, bias)
+            error = target - prediction
+            weights += learning_rate * error * np.array(inputs)
+            bias += learning_rate * error
+    return weights, bias
+
+
+
+def perceptron_accuracy(y, y_hat):
+    correct_predictions = sum([1 for true, pred in zip(y, y_hat) if true == pred])
+    accuracy = correct_predictions / len(y) * 100
+    return accuracy
+
+
+# Implement the neural network
+
+# Set the seed
+seed(1)
+
+# Load the csv file
+
+filename = 'moons.csv'
+dataset = load_csv(filename, skip=True)
+
+# Configure the perception with the bias, learning rate and epochs
+custom_split = 0.8
+custom_bias = 0
+custom_learning_rate = 0.1
+custom_epochs = 10000
+
+# Split the dataset for both training and testing
+
+X_train, y_train, X_test, y_test = train_test_split(dataset, split=custom_split)
+
+perceptron = Perceptron(input_size=2, bias=custom_bias, learning_rate=custom_learning_rate, epochs=custom_epochs)
+
+# Training
+weights, bias = train(X_train, y_train, perceptron.learning_rate, perceptron.epochs, perceptron.weights, perceptron.bias)
+
+# Predictions
+y_hat = []
+
+# Testing
+for i in range(len(X_test)):
+    prediction = predict(X_test[i], weights, bias)
+    y_hat.append(prediction)
+    print(f"Input: {X_test[i]}, Predicted: {prediction}, Actual: {y_test[i]}")
+
+# Test for Accuracy
+perceptron_accuracy(y_test, y_hat)

readme.md

@@ -6,7 +6,7 @@ Done:
 - [x] Q1
 - [x] Q2 
 - [x] Q3
-- [ ] Q4
+- [x] Q4
 ---
 
 * Submit once after init run
@@ -20,4 +20,6 @@ Done:
 ---
 <h1>Profit?</h1>
 
-![img.png](img.png)
+![img.png](img.png)
+
+<h1>30/30 BIG PROFIT</h1>