quantrs2-ml 0.1.3

use crate::error::{MLError, Result};
use crate::optimization::Optimizer;
use quantrs2_circuit::builder::Simulator;
use quantrs2_circuit::prelude::Circuit;
use quantrs2_sim::statevector::StateVectorSimulator;
use scirs2_core::ndarray::{Array1, Array2};
use scirs2_core::random::prelude::*;
use std::fmt;

/// Activation function types for quantum layers
#[derive(Debug, Clone, Copy, PartialEq)]
pub enum ActivationType {
    /// Linear activation (identity)
    Linear,
    /// ReLU activation
    ReLU,
    /// Sigmoid activation
    Sigmoid,
    /// Tanh activation
    Tanh,
}

/// Represents a layer type in a quantum neural network
#[derive(Debug, Clone)]
pub enum QNNLayerType {
    /// Encoding layer for converting classical data to quantum states
    EncodingLayer {
        /// Number of classical features to encode
        num_features: usize,
    },

    /// Variational layer with trainable parameters
    VariationalLayer {
        /// Number of trainable parameters
        num_params: usize,
    },

    /// Entanglement layer to create entanglement between qubits
    EntanglementLayer {
        /// Connectivity pattern, e.g., "full", "linear", "circular"
        connectivity: String,
    },

    /// Measurement layer to extract classical information
    MeasurementLayer {
        /// Measurement basis, e.g., "computational", "Pauli-X", "Pauli-Y", "Pauli-Z"
        measurement_basis: String,
    },
}

/// Results from training a quantum neural network
#[derive(Debug, Clone)]
pub struct TrainingResult {
    /// Final loss value after training
    pub final_loss: f64,

    /// Training accuracy (for classification tasks)
    pub accuracy: f64,

    /// Loss history during training
    pub loss_history: Vec<f64>,

    /// Optimal parameters found during training
    pub optimal_parameters: Array1<f64>,
}

/// Represents a quantum neural network
#[derive(Debug, Clone)]
pub struct QuantumNeuralNetwork {
    /// The layers that make up the network
    pub layers: Vec<QNNLayerType>,

    /// The number of qubits used in the network
    pub num_qubits: usize,

    /// The dimension of the input data
    pub input_dim: usize,

    /// The dimension of the output data
    pub output_dim: usize,

    /// Network parameters (weights)
    pub parameters: Array1<f64>,
}

impl QuantumNeuralNetwork {
    /// Creates a new quantum neural network
    pub fn new(
        layers: Vec<QNNLayerType>,
        num_qubits: usize,
        input_dim: usize,
        output_dim: usize,
    ) -> Result<Self> {
        // Validate the layers and structure
        if layers.is_empty() {
            return Err(MLError::ModelCreationError(
                "QNN must have at least one layer".to_string(),
            ));
        }

        // Determine parameter count from variational layers
        let num_params = layers
            .iter()
            .filter_map(|layer| match layer {
                QNNLayerType::VariationalLayer { num_params } => Some(num_params),
                _ => None,
            })
            .sum::<usize>();

        // Create random initial parameters
        let parameters = Array1::from_vec(
            (0..num_params)
                .map(|_| thread_rng().random::<f64>() * 2.0 * std::f64::consts::PI)
                .collect(),
        );

        Ok(QuantumNeuralNetwork {
            layers,
            num_qubits,
            input_dim,
            output_dim,
            parameters,
        })
    }

    /// Creates a quantum circuit representation of the network for a given input
    fn create_circuit(&self, input: &Array1<f64>) -> Result<Circuit<4>> {
        // In a real implementation, this would create a proper circuit based on the layers
        // For now, we'll create a dummy circuit with maximum 4 qubits to avoid memory issues
        let mut circuit = Circuit::<4>::new();

        // Apply dummy gates to demonstrate the concept
        for i in 0..self.num_qubits.min(4) {
            circuit.h(i)?;
        }

        Ok(circuit)
    }

    /// Runs the network on a given input
    pub fn forward(&self, input: &Array1<f64>) -> Result<Array1<f64>> {
        // For now, this is a dummy implementation
        let circuit = self.create_circuit(input)?;

        // Run the circuit
        let simulator = StateVectorSimulator::new();
        let _result = simulator.run(&circuit)?;

        // Process the result to get the output
        let output = Array1::zeros(self.output_dim);

        Ok(output)
    }

    /// Trains the network on a dataset
    pub fn train(
        &mut self,
        x_train: &Array2<f64>,
        y_train: &Array2<f64>,
        epochs: usize,
        learning_rate: f64,
    ) -> Result<TrainingResult> {
        // This is a dummy implementation
        let loss_history = vec![0.5, 0.4, 0.3, 0.25, 0.2];

        Ok(TrainingResult {
            final_loss: 0.2,
            accuracy: 0.85,
            loss_history,
            optimal_parameters: self.parameters.clone(),
        })
    }

    /// Trains the network on a dataset with 1D labels (compatibility method)
    pub fn train_1d(
        &mut self,
        x_train: &Array2<f64>,
        y_train: &Array1<f64>,
        epochs: usize,
        learning_rate: f64,
    ) -> Result<TrainingResult> {
        // Convert 1D labels to 2D
        let y_2d = y_train.clone().into_shape((y_train.len(), 1))?;
        self.train(x_train, &y_2d, epochs, learning_rate)
    }

    /// Predicts the output for a given input
    pub fn predict(&self, input: &Array1<f64>) -> Result<Array1<f64>> {
        self.forward(input)
    }

    /// Predicts the output for a batch of inputs
    pub fn predict_batch(&self, inputs: &Array2<f64>) -> Result<Array2<f64>> {
        let batch_size = inputs.nrows();
        let mut outputs = Array2::zeros((batch_size, self.output_dim));

        for (i, row) in inputs.axis_iter(scirs2_core::ndarray::Axis(0)).enumerate() {
            let input = row.to_owned();
            let output = self.predict(&input)?;
            outputs.row_mut(i).assign(&output);
        }

        Ok(outputs)
    }
}

/// Builder for quantum neural networks
#[derive(Debug, Clone)]
pub struct QNNBuilder {
    layers: Vec<QNNLayerType>,
    num_qubits: usize,
    input_dim: usize,
    output_dim: usize,
}

impl QNNBuilder {
    /// Creates a new QNN builder
    pub fn new() -> Self {
        QNNBuilder {
            layers: Vec::new(),
            num_qubits: 0,
            input_dim: 0,
            output_dim: 0,
        }
    }

    /// Sets the number of qubits
    pub fn with_qubits(mut self, num_qubits: usize) -> Self {
        self.num_qubits = num_qubits;
        self
    }

    /// Sets the input dimension
    pub fn with_input_dim(mut self, input_dim: usize) -> Self {
        self.input_dim = input_dim;
        self
    }

    /// Sets the output dimension
    pub fn with_output_dim(mut self, output_dim: usize) -> Self {
        self.output_dim = output_dim;
        self
    }

    /// Adds an encoding layer
    pub fn add_encoding_layer(mut self, num_features: usize) -> Self {
        self.layers
            .push(QNNLayerType::EncodingLayer { num_features });
        self
    }

    /// Adds a layer (alias for add_encoding_layer for compatibility)
    pub fn add_layer(self, size: usize) -> Self {
        self.add_encoding_layer(size)
    }

    /// Adds a variational layer
    pub fn add_variational_layer(mut self, num_params: usize) -> Self {
        self.layers
            .push(QNNLayerType::VariationalLayer { num_params });
        self
    }

    /// Adds an entanglement layer
    pub fn add_entanglement_layer(mut self, connectivity: &str) -> Self {
        self.layers.push(QNNLayerType::EntanglementLayer {
            connectivity: connectivity.to_string(),
        });
        self
    }

    /// Adds a measurement layer
    pub fn add_measurement_layer(mut self, measurement_basis: &str) -> Self {
        self.layers.push(QNNLayerType::MeasurementLayer {
            measurement_basis: measurement_basis.to_string(),
        });
        self
    }

    /// Builds the quantum neural network
    pub fn build(self) -> Result<QuantumNeuralNetwork> {
        if self.num_qubits == 0 {
            return Err(MLError::ModelCreationError(
                "Number of qubits must be greater than 0".to_string(),
            ));
        }

        if self.input_dim == 0 {
            return Err(MLError::ModelCreationError(
                "Input dimension must be greater than 0".to_string(),
            ));
        }

        if self.output_dim == 0 {
            return Err(MLError::ModelCreationError(
                "Output dimension must be greater than 0".to_string(),
            ));
        }

        if self.layers.is_empty() {
            return Err(MLError::ModelCreationError(
                "QNN must have at least one layer".to_string(),
            ));
        }

        QuantumNeuralNetwork::new(
            self.layers,
            self.num_qubits,
            self.input_dim,
            self.output_dim,
        )
    }
}

impl fmt::Display for QNNLayerType {
    fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
        match self {
            QNNLayerType::EncodingLayer { num_features } => {
                write!(f, "Encoding Layer (features: {})", num_features)
            }
            QNNLayerType::VariationalLayer { num_params } => {
                write!(f, "Variational Layer (parameters: {})", num_params)
            }
            QNNLayerType::EntanglementLayer { connectivity } => {
                write!(f, "Entanglement Layer (connectivity: {})", connectivity)
            }
            QNNLayerType::MeasurementLayer { measurement_basis } => {
                write!(f, "Measurement Layer (basis: {})", measurement_basis)
            }
        }
    }
}

/// Quantum neural network layer for use in other modules
#[derive(Debug, Clone)]
pub struct QNNLayer {
    /// Input dimension
    pub input_dim: usize,
    /// Output dimension
    pub output_dim: usize,
    /// Activation function
    pub activation: ActivationType,
}

impl QNNLayer {
    /// Create a new QNN layer
    pub fn new(input_dim: usize, output_dim: usize, activation: ActivationType) -> Self {
        Self {
            input_dim,
            output_dim,
            activation,
        }
    }
}