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package model.MARK_II.region;

/**
 * A data structure that partly represents the connection strength from a
 * Segment to an Cell. Each Synapse has an Cell and has a permanenceValue. A
 * Synapse with permanenceValue Greater than or equal to 0.2 means that it is connected to an Cell.
 * This creates a binary connection from a Synapse to an Cell. For example a
 * permanenceValue of 0.19 means a Synapse is not connected to an Cell whereas a
 * permanenceValue of 0.205 does. Furthermore, A permanceValue of 0.9 will only
 * be considered by the algorithm to be connected. The idea is that the
 * connection is now very "permanent" and not easy to forget. This
 * permanenceValue implementation strategy was done to make the learning
 * algorithms involving Synapses more efficient and minimally reduces the
 * learning algorithms performance.
 *  

 * There are 2 types of synapses in the brain. The MARK I Synapse data structure
 * models the chemical synapse and not the electrical synapse using the
 * following neuroscience ideas: 1) synaptic plasticity(also known as Hebbian
 * plasticity) = If neuron A repeatedly takes part in firing neuron B, then the
 * synapses from A to B are strengthened. 2) longterm potentiation/depression =
 * Experimentally observed increase/decrease in synaptic strength that lasts for
 * hours or days.
 *  
 * NOTE: When getting the activeState or previousActiveState of a Synapse make
 * sure to check that the Synapse is connected to it's Cell by calling
 * synapse.getConnectedState()
 *  
 * Input of Synapse: activity state of Cell.
 *  
 * Output of Synapse: whether this Synapse is connected or not to the Cell.
 *
 * @param  A synapse can be connected to either a SensorCell or a Neuron.
 * @author Quinn Liu ([email protected])
 * @author Michael Cogswell ([email protected])
 * @version June 8, 2013
 */
public class Synapse {
    /**
     * When a Synapse's Cell is active, the Synapse will create excitatory
     * post-synaptic potential (EPSPs). However, when a Synapse's Cell is not
     * active it will not contribute to the spiking of the post-synaptic Neuron
     * which is not inhibitory like an inhibitory post synaptic potential
     * (IPSPs).
     */
    private Cell cell;
    private double permanenceValue;
    private int cellColumn;
    private int cellRow;

    // class level variables that can be changed for all synapses
    /**
     * Amount permanenceValue is increased during one iteration of learning.
     */
    public static double PERMANENCE_INCREASE = 0.02;

    /**
     * Amount permanenceValue is decreased during one iteration of learning.
     */
    public static double PERMANENCE_DECREASE = 0.005;

    /**
     * Minimal permanenceValue needed for a Synapse to be connected.
     */
    public static double MINIMAL_CONNECTED_PERMANENCE = 0.2;

    public static double INITIAL_PERMANENCE = 0.3;

    /**
     * Create a new Synapse object with an Cell object and INITIAL_PERMANENCE
     * value.
     */
    public Synapse(Cell cell, int cellColumn, int cellRow) {
        if (cell == null) {
            throw new IllegalArgumentException(
                    "cell in Synapse class constructor cannot be null");
        } else if (cellColumn < 0 || cellRow < 0) {
            throw new IllegalArgumentException(
                    "cellColumn and cellRow in Synapse class constructor must be > 0");
        }
        this.cell = cell;
        this.permanenceValue = INITIAL_PERMANENCE;
        this.cellColumn = cellColumn;
        this.cellRow = cellRow;
    }

    /**
     * Create a new synapse object with an Cell object and given permanence
     * value.
     */
    public Synapse(Cell cell, double initialPermanence, int cellColumn,
                   int cellRow) {
        this(cell, cellColumn, cellRow);

        if (initialPermanence < 0.0 || initialPermanence > 1.0) {
            throw new IllegalArgumentException(
                    "initialPermanence in Synapse class constructor must be between 0 and 1");
        }
        this.permanenceValue = initialPermanence;
    }

    /**
     * @return This Synapse's Cell if it is connected. Otherwise return null.
     */
    public Cell getConnectedCell() {
        if (this.isConnected()) {
            return this.cell;
        } else {
            return null;
        }
    }

    public Cell getCell() {
        return this.cell;
    }

    /**
     * @return true if this Synapses' permanenceValue is greater than the
     * MINIMAL_CONNECTED_PERMANCE represented the Synapse and Cell are
     * connected.
     */
    public boolean isConnected() {
        return (this.permanenceValue >= MINIMAL_CONNECTED_PERMANENCE);
    }

    /**
     * Increase the permanenceValue of a Synapse object by PERMANENCE_INCREASE
     * while forcing permanenceValue to always be less than 1.0.
     *  
     * Simulates longterm potentiation in a real Synapse.
     */
    public void increasePermanence() {
        this.permanenceValue = Math.min(1.0, this.permanenceValue
                + PERMANENCE_INCREASE);
    }

    /**
     * Decrease the permanenceValue of a Synapse object by PERMANENCE_DECREASE
     * while forcing permanenceValue to always be greater than 0.0.
     *  
     * Simulates longterm depression in a real Synapse.
     */
    public void decreasePermanence() {
        this.permanenceValue = Math.max(0.0, this.permanenceValue
                - PERMANENCE_DECREASE);
    }

    public int getCellColumn() {
        return this.cellColumn;
    }

    public int getCellRow() {
        return this.cellRow;
    }

    @Override
    public String toString() {
        StringBuilder stringBuilder = new StringBuilder();
        Cell cell = this.getConnectedCell();
        stringBuilder.append("\n===========================");
        stringBuilder.append("\n----Synapse Information----");
        stringBuilder.append("\n Connected to a: ");
        stringBuilder.append(this.cell.toString());
        stringBuilder.append("\n     Located at (row, column): ");
        stringBuilder.append("(" + this.cellRow + ", ");
        stringBuilder.append(this.cellColumn + ")");
        stringBuilder.append("\npermanenceValue: ");
        stringBuilder.append(this.permanenceValue);
        stringBuilder.append("\n    isConnected: ");
        if (this.getConnectedCell() == null) {
            stringBuilder.append("false");
        } else {
            stringBuilder.append("true");
        }
        ;
        stringBuilder.append("\n===========================");
        String synapseInformation = stringBuilder.toString();
        return synapseInformation;
    }

    @Override
    public int hashCode() {
        final int prime = 31;
        int result = 1;
        result = prime * result + cellColumn;
        result = prime * result + cellRow;
        return result;
    }

    @Override
    public boolean equals(Object obj) {
        if (this == obj)
            return true;
        if (obj == null)
            return false;
        if (getClass() != obj.getClass())
            return false;
        Synapse other = (Synapse) obj;
        if (cellColumn != other.cellColumn)
            return false;
        if (cellRow != other.cellRow)
            return false;
        if (Double.doubleToLongBits(permanenceValue) != Double
                .doubleToLongBits(other.permanenceValue))
            return false;
        if (!this.cell.equals(other.cell)) {
            return false;
        }
        return true;
    }
    

    public double getPermanenceValue() {
        return this.permanenceValue;
    }

    public void setPermanenceValue(double permanenceValue) {
        this.permanenceValue = permanenceValue;
    }
}