Merge pull request #537 from htm-community/tm_conn

TM Connections integration

bindings/py/cpp_src/bindings/algorithms/py_Connections.cpp

@@ -49,10 +49,14 @@ R"(Compatibility Warning: This classes API is unstable and may change without wa
         [](const Connections &self) { return self.getConnectedThreshold(); });
 
     py_Connections.def("createSegment", &Connections::createSegment,
-        py::arg("cell"));
+        py::arg("cell"),
+	py::arg("maxSegmentsPerCell") = 0
+	);
 
     py_Connections.def("destroySegment", &Connections::destroySegment);
 
+    py_Connections.def("iteration", &Connections::iteration);
+
     py_Connections.def("createSynapse", &Connections::createSynapse,
         py::arg("segment"),
         py::arg("presynaticCell"),
@@ -93,24 +97,26 @@ R"(Compatibility Warning: This classes API is unstable and may change without wa
     py_Connections.def("reset", &Connections::reset);
 
     py_Connections.def("computeActivity",
-        [](Connections &self, SDR &activePresynapticCells) {
+        [](Connections &self, SDR &activePresynapticCells, bool learn=true) {
             // Allocate buffer to return & make a python destructor object for it.
             auto activeConnectedSynapses =
                 new std::vector<SynapseIdx>( self.segmentFlatListLength(), 0u );
             auto destructor = py::capsule( activeConnectedSynapses,
                 [](void *dataPtr) {
                     delete reinterpret_cast<std::vector<SynapseIdx>*>(dataPtr); });
-            // Call the C++ method.
-            self.computeActivity(*activeConnectedSynapses, activePresynapticCells.getSparse());
-            // Wrap vector in numpy array.
+
+	    // Call the C++ method.
+            self.computeActivity(*activeConnectedSynapses, activePresynapticCells.getSparse(), learn);
+
+	    // Wrap vector in numpy array.
             return py::array(activeConnectedSynapses->size(),
                              activeConnectedSynapses->data(),
                              destructor);
         },
 R"(Returns numActiveConnectedSynapsesForSegment)");
 
     py_Connections.def("computeActivityFull",
-        [](Connections &self, SDR &activePresynapticCells) {
+        [](Connections &self, SDR &activePresynapticCells, bool learn=true) {
             // Allocate buffer to return & make a python destructor object for it.
             auto activeConnectedSynapses =
                 new std::vector<SynapseIdx>( self.segmentFlatListLength(), 0u );
@@ -123,9 +129,13 @@ R"(Returns numActiveConnectedSynapsesForSegment)");
             auto potentialDestructor = py::capsule( activePotentialSynapses,
                 [](void *dataPtr) {
                     delete reinterpret_cast<std::vector<SynapseIdx>*>(dataPtr); });
-            // Call the C++ method.
-            self.computeActivity(*activeConnectedSynapses, *activePotentialSynapses,
-                                            activePresynapticCells.getSparse());
+
+	    // Call the C++ method.
+            self.computeActivity(*activeConnectedSynapses, 
+			         *activePotentialSynapses,
+                                 activePresynapticCells.getSparse(),
+				 learn);
+
             // Wrap vector in numpy array.
             return py::make_tuple(
                     py::array(activeConnectedSynapses->size(),

src/examples/hotgym/HelloSPTP.cpp

@@ -29,6 +29,7 @@
 #include "htm/types/Sdr.hpp"
 #include "htm/utils/Random.hpp"
 #include "htm/utils/MovingAverage.hpp"
+#include "htm/utils/SdrMetrics.hpp"
 
 namespace examples {
 
@@ -79,6 +80,13 @@ Real64 BenchmarkHotgym::run(UInt EPOCHS, bool useSPlocal, bool useSPglobal, bool
   SDR outTM(spGlobal.getColumnDimensions()); 
   Real an = 0.0f, anLikely = 0.0f; //for anomaly:
   MovingAverage avgAnom10(1000); //chose the window large enough so there's (some) periodicity in the patter, so TM can learn something
+
+  //metrics
+  Metrics statsInput(input, 1000);
+  Metrics statsSPlocal(outSPlocal, 1000);
+  Metrics statsSPglobal(outSPglobal, 1000);
+  Metrics statsTM(outTM, 1000);
+
   /*
    * For example: fn = sin(x) -> periodic >= 2Pi ~ 6.3 && x+=0.01 -> 630 steps to 1st period -> window >= 630
    */
@@ -147,13 +155,26 @@ Real64 BenchmarkHotgym::run(UInt EPOCHS, bool useSPlocal, bool useSPglobal, bool
     if (e == EPOCHS - 1) {
       tAll.stop();
 
+      //print connections stats
+      cout << "\nInput :\n" << statsInput
+	   << "\nSP(local) " << spLocal.connections
+	   << "\nSP(local) " << statsSPlocal
+           << "\nSP(global) " << spGlobal.connections
+	   << "\nSP(global) " << statsSPglobal
+           << "\nTM " << tm.connections 
+	   << "\nTM " << statsTM
+	   << "\n";
+
+      // output values
       cout << "Epoch = " << e << endl;
       cout << "Anomaly = " << an << endl;
       cout << "Anomaly (avg) = " << avgAnom10.getCurrentAvg() << endl;
       cout << "Anomaly (Likelihood) = " << anLikely << endl;
       cout << "SP (g)= " << outSP << endl;
       cout << "SP (l)= " << outSPlocal <<endl;
       cout << "TM= " << outTM << endl;
+
+      //timers
       cout << "==============TIMERS============" << endl;
       cout << "Init:\t" << tInit.getElapsed() << endl;
       cout << "Random:\t" << tRng.getElapsed() << endl;
@@ -184,12 +205,12 @@ Real64 BenchmarkHotgym::run(UInt EPOCHS, bool useSPlocal, bool useSPglobal, bool
 
       SDR goldTM({COLS});
       const SDR_sparse_t deterministicTM{
-        51, 62, 72, 77, 102, 155, 287, 306, 337, 340, 370, 493, 542, 952, 1089, 1110, 1115, 1193, 1463, 1488, 1507, 1518, 1547, 1626, 1668, 1694, 1781, 1803, 1805, 1827, 1841, 1858,1859, 1860, 1861, 1862, 1878, 1881, 1915, 1918, 1923, 1929, 1933, 1939, 1941, 1953, 1955, 1956, 1958, 1961, 1965, 1968, 1975, 1976, 1980, 1981, 1985, 1986, 1987, 1991, 1992, 1994, 1997, 2002, 2006, 2008, 2012, 2013, 2040, 2042
+        62, 77, 85, 322, 340, 432, 952, 1120, 1488, 1502, 1512, 1518, 1547, 1627, 1633, 1668, 1727, 1729, 1797, 1803, 1805, 1812, 1858, 1859, 1896, 1918, 1923, 1925, 1929, 1931, 1939, 1941, 1942, 1944, 1950, 1953, 1955, 1956, 1965, 1966, 1967, 1968, 1974, 1980, 1987, 1996, 2006, 2008, 2011, 2027, 2030, 2042, 2046
       };
       goldTM.setSparse(deterministicTM);
 
-      const float goldAn = 0.745098f;
-      const float goldAnAvg = 0.408286f;
+      const float goldAn    = 0.627451f;
+      const float goldAnAvg = 0.407265f;
 
       if(EPOCHS == 5000) { //these hand-written values are only valid for EPOCHS = 5000 (default), but not for debug and custom runs. 
         NTA_CHECK(input == goldEnc) << "Deterministic output of Encoder failed!\n" << input << "should be:\n" << goldEnc;