"""Module: memcomputing
This module implements memory-centric computing techniques to emulate quantum-like advantages on classical systems.
Future enhancements should include detailed inline documentation and type annotations for enhanced clarity and maintainability.
"""

import numpy as np

class MemComputingElement:
    def __init__(self, state=0.0, weights=None):
        self.state = state
        if weights is None:
            self.weights = np.random.uniform(-1, 1)
        else:
            self.weights = weights

    def compute(self, inputs):
        # Simulate in-memory computation by updating the state based on inputs
        # Simple example using weighted sum and activation function
        self.state = self.activation(np.dot(self.weights, inputs))
        return self.state

    def activation(self, x):
        # Activation function (e.g., hyperbolic tangent)
        return np.tanh(x)

class MemComputingNetwork:
    def __init__(self, size):
        self.size = size
        self.elements = [MemComputingElement() for _ in range(size)]
        self.connections = np.random.uniform(-1, 1, (size, size))  # Initialize connections

    def run(self, inputs, steps=10):
        outputs = []
        for _ in range(steps):
            current_states = np.array([element.state for element in self.elements])
            new_states = []
            for i, element in enumerate(self.elements):
                input_signal = self.connections[i] @ current_states + inputs[i]
                new_state = element.compute(input_signal)
                new_states.append(new_state)
            outputs.append(new_states)
        return outputs

class MemoryCentricProcessor:
    def __init__(self, config=None):
        self.config = config or {}
        self.data_cleaner = DataCleaner()
        self.data_transformer = DataTransformer(config=self.config)
        self.optimizer = Optimizer()

    def process_data(self, raw_data):
        """
        Robust data processing method.
        """
        # Implement data preprocessing, cleaning, and transformation
        processed_data = self._preprocess(raw_data)
        return processed_data

    def optimize(self, processed_data):
        """
        Optimization methods to enhance data processing efficiency.
        """
        # Implement optimization algorithms
        optimized_data = self._optimize_data(processed_data)
        return optimized_data

    def _preprocess(self, data):
        """
        Internal method to preprocess data.
        """
        # Example preprocessing steps
        cleaned_data = self._clean_data(data)
        transformed_data = self._transform_data(cleaned_data)
        return transformed_data

    def _clean_data(self, data):
        """
        Internal method to clean data.
        """
        # Implement data cleaning logic
        cleaned_data = self.data_cleaner.clean(data)
        return cleaned_data

    def _transform_data(self, data):
        """
        Internal method to transform data.
        """
        # Implement data transformation logic
        transformed_data = self.data_transformer.transform(data)
        return transformed_data

    def _optimize_data(self, data):
        """
        Internal method to optimize data processing.
        """
        # Implement optimization logic
        optimized_data = self.optimizer.optimize(data)
        return optimized_data

# Additional Classes for Data Processing and Optimization

class DataCleaner:
    def clean(self, data):
        """
        Clean raw data by handling missing values and removing duplicates.
        """
        # Example cleaning steps
        if isinstance(data, dict):
            # Remove key-value pairs with None values
            cleaned_data = {k: v for k, v in data.items() if v is not None}
        elif isinstance(data, list):
            # Remove None entries
            cleaned_data = [item for item in data if item is not None]
        else:
            cleaned_data = data  # Placeholder for other data types
        return cleaned_data

class DataTransformer:
    def __init__(self, config=None):
        self.config = config or {}  # Initialize config with an empty dict if not provided

    def transform(self, data):
        """
        Transform data by normalizing numerical values.
        """
        # Example transformation: normalize numerical data
        if isinstance(data, dict):
            transformed_data = {k: self._normalize(v) if isinstance(v, (int, float)) else v for k, v in data.items()}
        elif isinstance(data, list):
            transformed_data = [self._normalize(item) if isinstance(item, (int, float)) else item for item in data]
        else:
            transformed_data = data  # Placeholder for other data types
        return transformed_data

    def _normalize(self, value):
        """
        Normalize a numerical value to a range between 0 and 1.
        """
        # Simple normalization (requires min and max; placeholders used here)
        min_val = self.config.get('min_val', 0)
        max_val = self.config.get('max_val', 1)
        if max_val - min_val == 0:
            return 0
        return (value - min_val) / (max_val - min_val)

class Optimizer:
    def optimize(self, data):
        """
        Optimize data processing by applying a simple optimization algorithm.
        """
        # Example optimization: reduce data size by sampling
        if isinstance(data, list) and len(data) > 100:
            optimized_data = data[:100]  # Sample first 100 items
        else:
            optimized_data = data
        return optimized_data

# Unit Tests
import unittest

class TestMemoryCentricProcessor(unittest.TestCase):
    def setUp(self):
        self.config = {'param1': 'value1', 'param2': 'value2', 'min_val': 0, 'max_val': 100}
        self.processor = MemoryCentricProcessor(config=self.config)

    def test_process_data(self):
        raw_data = {"key1": 50, "key2": None, "key3": 75}  # Example raw data
        processed = self.processor.process_data(raw_data)
        expected = {"key1": 0.5, "key3": 0.75}
        self.assertEqual(processed, expected)

    def test_optimize(self):
        processed_data = list(range(150))  # Example processed data
        optimized = self.processor.optimize(processed_data)
        expected = list(range(100))
        self.assertEqual(optimized, expected)

    def test_clean_data(self):
        data = {"a": 1, "b": None, "c": 3}
        cleaned = self.processor._clean_data(data)
        expected = {"a": 1, "c": 3}
        self.assertEqual(cleaned, expected)

    def test_transform_data(self):
        self.processor.data_transformer.config = {'min_val': 0, 'max_val': 100}
        data = {"a": 25, "b": 50, "c": 75}
        transformed = self.processor._transform_data(data)
        expected = {"a": 0.25, "b": 0.5, "c": 0.75}
        self.assertEqual(transformed, expected)

    def test_optimize_data(self):
        data = list(range(150))
        optimized = self.processor._optimize_data(data)
        expected = list(range(100))
        self.assertEqual(optimized, expected)

if __name__ == "__main__":
    unittest.main()