import numpy as np
from sklearn.base import BaseEstimator, ClassifierMixin
from sklearn.utils.validation import check_X_y, check_array, check_is_fitted
from sklearn.neural_network import MLPClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from scipy.stats import entropy

class AdaptiveRandomnessNetwork(BaseEstimator, ClassifierMixin):
    def __init__(self, randomness_threshold=0.7, max_depth=5, hidden_layer_sizes=(100,), 
                 mlp_alpha=0.0001, dt_min_samples_split=2, n_estimators=100):
        self.randomness_threshold = randomness_threshold
        self.max_depth = max_depth
        self.hidden_layer_sizes = hidden_layer_sizes
        self.mlp_alpha = mlp_alpha
        self.dt_min_samples_split = dt_min_samples_split
        self.n_estimators = n_estimators
        self.model = None

    def _compute_randomness(self, X):
        entropies = []
        for feature in X.T:
            _, counts = np.unique(feature, return_counts=True)
            prob = counts / len(feature)
            entropies.append(entropy(prob, base=2) / np.log2(len(prob)))
        return np.mean(entropies)

    def fit(self, X, y):
        X, y = check_X_y(X, y)
        self.classes_ = np.unique(y)
        
        randomness = self._compute_randomness(X)
        
        if randomness < self.randomness_threshold / 2:
            # For highly structured data, use a more complex model
            self.model = MLPClassifier(
                hidden_layer_sizes=self.hidden_layer_sizes,
                alpha=self.mlp_alpha,
                max_iter=1000,
                random_state=42
            )
        elif randomness < self.randomness_threshold:
            # For moderately structured data, use a decision tree
            self.model = DecisionTreeClassifier(
                max_depth=self.max_depth,
                min_samples_split=self.dt_min_samples_split,
                random_state=42
            )
        else:
            # For highly random data, use a random forest
            self.model = RandomForestClassifier(
                n_estimators=self.n_estimators,
                max_depth=self.max_depth,
                min_samples_split=self.dt_min_samples_split,
                random_state=42
            )
        
        self.model.fit(X, y)
        self.is_fitted_ = True
        return self

    def predict(self, X):
        check_is_fitted(self, 'is_fitted_')
        X = check_array(X)
        return self.model.predict(X)

    def predict_proba(self, X):
        check_is_fitted(self, 'is_fitted_')
        X = check_array(X)
        return self.model.predict_proba(X)

    def score(self, X, y):
        return self.model.score(X, y)

    def get_params(self, deep=True):
        return {
            "randomness_threshold": self.randomness_threshold,
            "max_depth": self.max_depth,
            "hidden_layer_sizes": self.hidden_layer_sizes,
            "mlp_alpha": self.mlp_alpha,
            "dt_min_samples_split": self.dt_min_samples_split,
            "n_estimators": self.n_estimators
        }

    def set_params(self, **parameters):
        for parameter, value in parameters.items():
            setattr(self, parameter, value)
        return self