[MINOR] Additional test script for learned sampling

This script clusters data points by their gradients (with sample size
centroids) and then samples one point cluster. On the adult dataset
the results were non-conclusive compared to random sampling.

Author: mboehm7

scripts/staging/learnedSampling/5_ClusteredSampling.dml

@@ -0,0 +1,64 @@
+#-------------------------------------------------------------
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+#
+#-------------------------------------------------------------
+
+# clustered gradients (no better than random sampling)
+# Accuracy (%): 88.39168202158463
+# Accuracy (%): 83.76164636019249
+# Accuracy (%): 96.04281828551373
+# Accuracy (%): 78.03829220845705
+# Accuracy (%): 100.0
+# Accuracy (%): 66.66325381386301
+
+# clustered data (no better than random sampling)
+# Accuracy (%): 86.95270685268054
+# Accuracy (%): 83.3316269069315
+# Accuracy (%): 96.472755988418
+# Accuracy (%): 73.82000614313505
+# Accuracy (%): 100.0
+# Accuracy (%): 69.74505989556671
+
+X = read("data/Adult_X.csv")
+y = read("data/Adult_y.csv")
+B = read("data/Adult_W.csv")
+
+[Xtrain,Xtest,ytrain,ytest] = split(X=X,Y=y,f=0.7,cont=FALSE,seed=7)
+
+sf = matrix("0.1 0.01 0.001", rows=3, cols=1)
+for(i in 1:nrow(sf)) {
+  sfi = as.scalar(sf[i]);
+
+  w = B[1:ncol(X), ];
+  icpt = as.scalar(B[nrow(B),])
+  Xgrad = sigmoid(Xtrain %*% w + icpt)-(ytrain == 1)
+  [C,Y]=kmeans(X=Xgrad, k=sfi*nrow(Xtrain), seed=7)
+
+  Yone = table(seq(1,nrow(Xtrain)),Y)
+  I = rowIndexMax(Yone); #pick first in every cluster
+  P = table(seq(1,nrow(I)), I, nrow(I), nrow(Xtrain))
+  P = removeEmpty(target=P, margin="rows");
+  Xtrain2 = P %*% Xtrain
+  ytrain2 = P %*% ytrain
+  B = multiLogReg(X=Xtrain2, Y=ytrain2, maxii=50, icpt=2, reg=0.001, verbose=FALSE);
+
+  [M,yhat,acc] = multiLogRegPredict(X=Xtrain2, B=B, Y=ytrain2, verbose=TRUE);
+  [M,yhat,acc] = multiLogRegPredict(X=Xtest, B=B, Y=ytest, verbose=TRUE);
+}
+