Modify RWD to use np svd

idaholab · Jul 24, 2023 · 76a9732 · 76a9732
1 parent b39f8ec
commit 76a9732
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Showing 2 changed files with 12 additions and 12 deletions.
diff --git a/ravenframework/SupervisedLearning/SupervisedLearning.py b/ravenframework/SupervisedLearning/SupervisedLearning.py
@@ -795,16 +795,16 @@ def finalizeGlobalRomSegmentEvaluation(self, settings, evaluation, weights, slic
     return evaluation
   ### END ROM Clustering ###
 
-  # @abc.abstractmethod
-  # def _train(self,featureVals,targetVals):
-  #   """
-  #     Perform training on samples in featureVals with responses y.
-  #     For an one-class model, +1 or -1 is returned.
-  #     @ In, featureVals, {array-like, sparse matrix}, shape=[n_samples, n_features],
-  #       an array of input feature values
-  #     @ Out, targetVals, array, shape = [n_samples], an array of output target
-  #       associated with the corresponding points in featureVals
-  #   """
+  @abc.abstractmethod
+  def _train(self,featureVals,targetVals):
+    """
+      Perform training on samples in featureVals with responses y.
+      For an one-class model, +1 or -1 is returned.
+      @ In, featureVals, {array-like, sparse matrix}, shape=[n_samples, n_features],
+        an array of input feature values
+      @ Out, targetVals, array, shape = [n_samples], an array of output target
+        associated with the corresponding points in featureVals
+    """
 
   @abc.abstractmethod
   def __confidenceLocal__(self,featureVals):

diff --git a/ravenframework/TSA/RWD.py b/ravenframework/TSA/RWD.py
@@ -146,10 +146,10 @@ def fit(self, signal, pivot, targets, settings):
         baseMatrix = np.zeros((signatureWindowLength, windowNumber))
         for i in range(windowNumber-1):
           baseMatrix[:,i] = np.copy(history[i*signatureWindowLength:(i+1)*signatureWindowLength])
-      U, s, V = mathUtils.computeTruncatedSingularValueDecomposition(baseMatrix,0)
+      U,s,V = np.linalg.svd(baseMatrix, full_matrices=True)
       featureMatrix = U.T @ signatureMatrix
       params[target] = {'uVec'   : U[:,0:fi],
-                        'Feature': featureMatrix}
+                        'Feature': featureMatrix[0:fi,:]}
     return params
 
   def getParamNames(self, settings):