revert pattern

When learnPattern is set, use learnPattern.
inferPattern is set, use inferPattern

src/htm/regions/ClassifierRegion.cpp

@@ -79,6 +79,8 @@ 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},
       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",
@@ -140,8 +142,8 @@ Dimensions ClassifierRegion::askImplForOutputDimensions(const std::string &name)
 
 
 void ClassifierRegion::compute() {
+  SDR &pattern = getInput("pattern")->getData().getSDR();
   if (learn_) {
-    SDR &learnPattern = getInput("learnPattern")->getData().getSDR();
     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).
@@ -165,14 +167,25 @@ void ClassifierRegion::compute() {
       }
       categoryIdxList.push_back(c);
     }
-    classifier_->learn(learnPattern, 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 = classifier_->infer(inferPattern);
+  //
+  PDF pdf;
+  if (inferPattern.size == 0) {
+    pdf = classifier_->infer(pattern);
+  } else {
+    pdf = classifier_->infer(inferPattern);
+  }
 
   // Adjust the buffer size to match the pdf.
   if (getOutput("pdf")->getData().getCount() < pdf.size()) {