/// <summary>
        /// Performs the remote initialization ot the SP on actor nodes.
        /// </summary>
        /// <param name="c"></param>
        /// <param name="distMem"></param>
        public override void InitMatrices(Connections c, DistributedMemory distMem)
        {
            IHtmDistCalculus remoteHtm = distMem?.ColumnDictionary as IHtmDistCalculus;

            if (remoteHtm == null)
            {
                throw new ArgumentException("Must implement IHtmDistCalculus!");
            }

            this.distMemConfig = distMem;

            SparseObjectMatrix <Column> mem = (SparseObjectMatrix <Column>)c.HtmConfig.Memory;

            c.HtmConfig.Memory = mem == null ? mem = new SparseObjectMatrix <Column>(c.HtmConfig.ColumnDimensions, dict: distMem.ColumnDictionary) : mem;

            c.HtmConfig.InputMatrix = new SparseBinaryMatrix(c.HtmConfig.InputDimensions);

            // Initiate the topologies
            c.HtmConfig.ColumnTopology = new Topology(c.HtmConfig.ColumnDimensions);
            c.HtmConfig.InputTopology  = new Topology(c.HtmConfig.InputDimensions);

            //Calculate numInputs and numColumns
            int numInputs  = c.HtmConfig.InputMatrix.GetMaxIndex() + 1;
            int numColumns = c.HtmConfig.Memory.GetMaxIndex() + 1;

            if (numColumns <= 0)
            {
                throw new ArgumentException("Invalid number of columns: " + numColumns);
            }
            if (numInputs <= 0)
            {
                throw new ArgumentException("Invalid number of inputs: " + numInputs);
            }
            c.HtmConfig.NumInputs  = numInputs;
            c.HtmConfig.NumColumns = numColumns;

            if (distMem != null)
            {
                //var distHtmCla = distMem.ColumnDictionary as HtmSparseIntDictionary<Column>;
                var distHtmCla = distMem.ColumnDictionary;// as ActorSbDistributedDictionaryBase<Column>;

                distHtmCla.HtmConfig = c.HtmConfig;
            }

            //
            // Fill the sparse matrix with column objects
            //var numCells = c.getCellsPerColumn();

            // var partitions = mem.GetPartitions();


            List <KeyPair> colList = new List <KeyPair>();

            for (int i = 0; i < numColumns; i++)
            {
                //colList.Add(new KeyPair() { Key = i, Value = new Column(numCells, i, c.getSynPermConnected(), c.NumInputs) });
                colList.Add(new KeyPair()
                {
                    Key = i, Value = c.HtmConfig
                });
            }

            Stopwatch sw = new Stopwatch();

            sw.Start();

            remoteHtm.InitializeColumnPartitionsDist(colList);

            //mem.set(colList);

            sw.Stop();
            //c.setPotentialPools(new SparseObjectMatrix<Pool>(c.getMemory().getDimensions(), dict: distMem == null ? null : distMem.PoolDictionary));

            Debug.WriteLine($" Upload time: {sw.ElapsedMilliseconds}");

            // Already initialized by creating of columns.
            //c.setConnectedMatrix(new SparseBinaryMatrix(new int[] { numColumns, numInputs }));

            //Initialize state meta-management statistics
            c.HtmConfig.OverlapDutyCycles    = new double[numColumns];
            c.HtmConfig.ActiveDutyCycles     = new double[numColumns];
            c.HtmConfig.MinOverlapDutyCycles = new double[numColumns];
            c.HtmConfig.MinActiveDutyCycles  = new double[numColumns];
            c.BoostFactors = (new double[numColumns]);
            ArrayUtils.FillArray(c.BoostFactors, 1);
        }
Пример #2
0
 /// <summary>
 /// Uses the same implementation as Single-Threaded.
 /// </summary>
 /// <param name="c"></param>
 /// <param name="distMem"></param>
 public override void InitMatrices(Connections c, DistributedMemory distMem)
 {
     base.InitMatrices(c, distMem);
 }