import numpy as np
from scipy.spatial.distance import cdist
from sklearn.base import BaseEstimator, ClassifierMixin
from sklearn.utils.validation import check_X_y, check_array, check_is_fitted
from sklearn.utils.multiclass import unique_labels

class AdaptiveKernelComplexity(BaseEstimator, ClassifierMixin):
    def __init__(self, initial_gamma=1.0, adaptation_rate=0.1, max_iterations=100):
        self.initial_gamma = initial_gamma
        self.adaptation_rate = adaptation_rate
        self.max_iterations = max_iterations

    def fit(self, X, y):
        # Check that X and y have correct shape
        X, y = check_X_y(X, y)
        
        # Store the classes seen during fit
        self.classes_ = unique_labels(y)
        
        # Store the training data
        self.X_ = X
        self.y_ = y
        
        # Initialize gamma
        self.gamma_ = self.initial_gamma
        
        # Adaptive process
        for _ in range(self.max_iterations):
            old_gamma = self.gamma_
            
            # Compute kernel matrix
            K = self._compute_kernel(X, X)
            
            # Compute predictions
            y_pred = np.sign(K.dot(y))
            
            # Compute error
            error = np.mean(y_pred != y)
            
            # Adapt gamma
            if error > 0.5:  # If error is high, increase complexity
                self.gamma_ *= (1 + self.adaptation_rate)
            else:  # If error is low, decrease complexity
                self.gamma_ *= (1 - self.adaptation_rate)
            
            # Check for convergence
            if np.abs(self.gamma_ - old_gamma) < 1e-6:
                break
        
        return self

    def predict(self, X):
        # Check is fit had been called
        check_is_fitted(self)

        # Input validation
        X = check_array(X)

        # Compute kernel between X and training data
        K = self._compute_kernel(X, self.X_)
        
        # Make predictions
        y_pred = np.sign(K.dot(self.y_))
        
        return y_pred

    def _compute_kernel(self, X1, X2):
        # Compute Gaussian kernel
        return np.exp(-self.gamma_ * cdist(X1, X2, metric='sqeuclidean'))