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import std.array: Appender, isArray;
import std.random: uniform;
import std.stdio: writefln;
import std.string: toLower;
import std.range: ElementEncodingType;

class MarkovChain(T) if (isArray!T)
{
    alias E = ElementEncodingType!T; // element type
    alias iT = immutable(T);
    alias iE = immutable(E);

    int order;
    double[iE][iT] probMap; // keys must be immutable

    this(T seqData, int order)
    {
        this.order = order;
        probMap = createMap(seqData);
    }

    // Creates a structure similar to a stochastic matrix, but uses
    // associative arrays to save space.
    double[iE][iT] createMap(T seqData)
    {
        // probMap is first used to hold the frequencies of transitions
        // occurring, but later holds the probabilities of transitions
        // occurring. This is done to save memory.
        double[iE][iT] probMap;
        int[iT] srcFreq;

        for(int i = 0; i < seqData.length-order; i++)
        {
            int j;

            for(j = 0; j < order; j++)
        {
        alias cur = seqData[i..i+order];
                srcFreq[cur] = srcFreq.get(cur, 0) + 1;
                probMap[cur] = probMap.get(cur, null); // AAs are initially null
                probMap[cur][next] = probMap[cur].get(next, 0) + 1;
        }
        }

        // Turn transition frequencies into transition probabilities.
        foreach(src, dstMap; probMap)
            foreach(dst, ref freq; dstMap)
                freq = freq/srcFreq[src];

        return probMap;
    }
}