add learnPattern ClassifierRegion

src/htm/algorithms/SDRClassifier.cpp

@@ -52,7 +52,7 @@ PDF Classifier::infer(const SDR & pattern) const {
     NTA_WARN << "Classifier: must call `learn` before `infer`.";
     return PDF(numCategories_, std::nan("")); //empty array []
   }
-  NTA_ASSERT(pattern.size == dimensions_) << "Input SDR does not match previously seen size!";
+  NTA_CHECK(pattern.size == dimensions_) << "Input SDR does not match previously seen size!";
 
   // Accumulate feed forward input.
   PDF probabilities( numCategories_, 0.0f );

src/htm/regions/ClassifierRegion.cpp

@@ -78,16 +78,20 @@ namespace htm {
     },
     inputs: {
       bucket:  { description: "The quantized value of the current sample, one from each encoder if more than one, for the learn step",
-                           type: Real64,  count: 0},  
-      pattern: { description:  "An SDR output bit pattern for a sample.  Usually the output of the SP or TM. For example: activeCells from TM",  
-                           type: SDR, count: 0} 
-    }, 
+                           type: Real64,  count: 0},
+      pattern: { description:  "An SDR output bit pattern for a sample.  Usually the output of the SP or TM. For example: activeCells from TM",
+                           type: SDR, count: 0},
+      inferPattern: { description:  "An SDR output bit pattern for a sample.  Usually the output of the SP or TM. For example: predictiveCells from TM",
+                           type: SDR, count: 0},
+      learnPattern: { description:  "An SDR output bit pattern for a sample.  Usually the output of the SP or TM. For example: activeCells from TM",
+                           type: SDR, count: 0}
+    },
     outputs: {
       pdf:       { description: "probability distribution function (pdf) for each category or bucket. Sorted by title.  Warning, buffer length will grow.",
                            type: Real64, count: 0},
       titles:    { description: "Quantized values of used samples which are the Titles corresponding to the pdf indexes. Sorted by title. Warning, buffer length will grow.",
                            type: Real64, count: 0},
-      predicted: { description: "An index (into pdf and titles) with the highest probability of being the match with the current pattern.",
+      predicted: { description: "An index (into pdf and titles) with the highest probability of being the match with the current inferPattern.",
                            type: UInt32, count: 1}
     }
    }
@@ -139,16 +143,13 @@ Dimensions ClassifierRegion::askImplForOutputDimensions(const std::string &name)
 
 void ClassifierRegion::compute() {
   SDR &pattern = getInput("pattern")->getData().getSDR();
-  // Note: if there is no link to 'pattern' input, the 'pattern' SDR length is 0
-  //       and SDRClassifier::infer() will throw an exception.
-
   if (learn_) {
     Array &b = getInput("bucket")->getData();
     // 'bucket' is a list of quantized samples being processed for this iteration.
     // There are one of these for each encoder (or value being encoded).
     // The values might not be consecutive, or in different ranges, or different things entirely.
     // We build a map and a corresponding vector containing the quantized samples actually used.
-    // This vector becomes the titles. The index into this list will be a consecutive list that 
+    // This vector becomes the titles. The index into this list will be a consecutive list that
     // we can presented to the Classifier which produces the pdf.  Note that the indexes used
     // by the classifier are not sorted by title but rather by the order in which an index is first seen.
     std::vector<UInt> categoryIdxList;
@@ -166,9 +167,25 @@ void ClassifierRegion::compute() {
       }
       categoryIdxList.push_back(c);
     }
-    classifier_->learn(pattern, categoryIdxList);
+
+    SDR &learnPattern = getInput("learnPattern")->getData().getSDR();
+    if (learnPattern.size == 0) {
+      classifier_->learn(pattern, categoryIdxList);
+    } else {
+      classifier_->learn(learnPattern, categoryIdxList);
+    }
+  }
+
+  SDR &inferPattern = getInput("inferPattern")->getData().getSDR();
+  // Note: if there is no link to 'inferPattern' input, the 'inferPattern' SDR length is 0
+  //       and SDRClassifier::infer() will throw an exception.
+  //
+  PDF pdf;
+  if (inferPattern.size == 0) {
+    pdf = classifier_->infer(pattern);
+  } else {
+    pdf = classifier_->infer(inferPattern);
   }
-  PDF pdf = classifier_->infer(pattern);
 
   // Adjust the buffer size to match the pdf.
   if (getOutput("pdf")->getData().getCount() < pdf.size()) {
@@ -189,7 +206,7 @@ void ClassifierRegion::compute() {
     size_t i = itm.second;
     if (pdf[i] > m) {
       m = pdf[i];
-      predicted[0] = static_cast<UInt32>(j);  // index of the quantized sample with the highest probability of matching the pattern
+      predicted[0] = static_cast<UInt32>(j);  // index of the quantized sample with the highest probability of matching the inferPattern
     }
     out[j] = pdf[i];
     titles[j] = bucketList[i];