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PSONeuralGas Classifier.py
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PSONeuralGas Classifier.py
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# -*- coding: utf-8 -*-
"""
Created on Wed Jun 19 00:44:26 2024
@author: S.M.H Mousavi
"""
# Load libraries
from pyswarm import pso
from sklearn.datasets import load_iris
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
import numpy as np
# Define the Neural Gas Model
class SupervisedNeuralGas:
def __init__(self, n_units=100, max_iter=100, eta_start=0.5, eta_end=0.01, lambda_start=30, lambda_end=0.1):
self.n_units = n_units
self.max_iter = max_iter
self.eta_start = eta_start
self.eta_end = eta_end
self.lambda_start = lambda_start
self.lambda_end = lambda_end
self.units = np.random.rand(n_units, 4) # Assuming iris dataset
self.unit_labels = np.zeros(n_units)
def train(self, data, labels):
for i in range(self.max_iter):
print(f"Training iteration {i+1}")
# Simulated training logic
def predict(self, data):
return np.random.randint(0, 3, size=data.shape[0])
# Objective function for PSO
def objective_function(params):
n_units, eta_start = int(params[0]), params[1]
model = SupervisedNeuralGas(n_units=n_units, eta_start=eta_start)
model.train(X_train, y_train)
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print(f"Current params: {params}, Accuracy: {accuracy}")
return -accuracy
# Load and preprocess data
iris = load_iris()
data = iris.data
target = iris.target
scaler = StandardScaler()
data_normalized = scaler.fit_transform(data)
X_train, X_test, y_train, y_test = train_test_split(data_normalized, target, test_size=0.2, random_state=42)
# Set bounds for PSO and execute
lb = [3, 0.01] # Lower bounds of n_units and eta_start
ub = [50, 0.9] # Upper bounds
xopt, fopt = pso(objective_function, lb, ub, swarmsize=50, omega=0.5, phip=0.5, phig=0.5, maxiter=100, debug=True)
print("Best parameters found: ", xopt)
print("Best accuracy achieved: ", -fopt)