[mlpack-svn] r15453 - in mlpack/conf/jenkins-conf/benchmark: . methods/scikit methods/shogun

fastlab-svn at coffeetalk-1.cc.gatech.edu fastlab-svn at coffeetalk-1.cc.gatech.edu
Thu Jul 11 11:46:45 EDT 2013


Author: marcus
Date: Thu Jul 11 11:46:44 2013
New Revision: 15453

Log:
Adjust functions, because Shogun libary changed some function in the new version.

Modified:
   mlpack/conf/jenkins-conf/benchmark/methods/scikit/kmeans.py
   mlpack/conf/jenkins-conf/benchmark/methods/shogun/allknn.py
   mlpack/conf/jenkins-conf/benchmark/methods/shogun/nbc.py
   mlpack/conf/jenkins-conf/benchmark/small_config.yaml

Modified: mlpack/conf/jenkins-conf/benchmark/methods/scikit/kmeans.py
==============================================================================
--- mlpack/conf/jenkins-conf/benchmark/methods/scikit/kmeans.py	(original)
+++ mlpack/conf/jenkins-conf/benchmark/methods/scikit/kmeans.py	Thu Jul 11 11:46:44 2013
@@ -87,10 +87,10 @@
         kmeans = KMeans(k=centroids.shape[1], init=centroids, n_init=1, 
             max_iter=m)
       elif seed:
-        kmeans = KMeans(k=int(clusters.group(1)), init='random', n_init=1, 
+        kmeans = KMeans(n_clusters=int(clusters.group(1)), init='random', n_init=1, 
             max_iter=m, random_state=int(seed.group(1)))
       else:
-        kmeans = KMeans(k=int(clusters.group(1)), n_init=1, max_iter=m)      
+        kmeans = KMeans(n_clusters=int(clusters.group(1)), n_init=1, max_iter=m)      
 
       kmeans.fit(data)
       labels = kmeans.labels_

Modified: mlpack/conf/jenkins-conf/benchmark/methods/shogun/allknn.py
==============================================================================
--- mlpack/conf/jenkins-conf/benchmark/methods/shogun/allknn.py	(original)
+++ mlpack/conf/jenkins-conf/benchmark/methods/shogun/allknn.py	Thu Jul 11 11:46:44 2013
@@ -20,9 +20,9 @@
 from timer import *
 
 import numpy as np
-from shogun.Features import RealFeatures, Labels
+from shogun.Features import RealFeatures, MulticlassLabels
 from shogun.Classifier import KNN as SKNN
-from shogun.Distance import EuclidianDistance
+from shogun.Distance import EuclideanDistance
 
 '''
 This class implements the All K-Nearest-Neighbors benchmark.
@@ -66,7 +66,7 @@
       referenceData = np.genfromtxt(self.dataset, delimiter=',')
 
     # Labels are the last row of the dataset.
-    labels = Labels(referenceData[:, (referenceData.shape[1] - 1)])
+    labels = MulticlassLabels(referenceData[:, (referenceData.shape[1] - 1)])
     referenceData = referenceData[:,:-1]
 
     with totalTimer:
@@ -83,7 +83,7 @@
           return -1
 
       referenceFeat = RealFeatures(referenceData.T)
-      distance = EuclidianDistance(referenceFeat, referenceFeat)
+      distance = EuclideanDistance(referenceFeat, referenceFeat)
 
       # Perform All K-Nearest-Neighbors.
       model = SKNN(k, distance, labels)

Modified: mlpack/conf/jenkins-conf/benchmark/methods/shogun/nbc.py
==============================================================================
--- mlpack/conf/jenkins-conf/benchmark/methods/shogun/nbc.py	(original)
+++ mlpack/conf/jenkins-conf/benchmark/methods/shogun/nbc.py	Thu Jul 11 11:46:44 2013
@@ -20,7 +20,7 @@
 from timer import *
 
 import numpy as np
-from shogun.Features import RealFeatures, Labels
+from shogun.Features import RealFeatures, MulticlassLabels
 from shogun.Classifier import GaussianNaiveBayes
 
 '''
@@ -59,7 +59,7 @@
     testData = np.genfromtxt(self.dataset[1], delimiter=',')
 
     # Labels are the last row of the training set.
-    labels = Labels(trainData[:, (trainData.shape[1] - 1)])
+    labels = MulticlassLabels(trainData[:, (trainData.shape[1] - 1)])
 
     with totalTimer:
       # Transform into features.

Modified: mlpack/conf/jenkins-conf/benchmark/small_config.yaml
==============================================================================
--- mlpack/conf/jenkins-conf/benchmark/small_config.yaml	(original)
+++ mlpack/conf/jenkins-conf/benchmark/small_config.yaml	Thu Jul 11 11:46:44 2013
@@ -82,7 +82,7 @@
         format: [csv, txt]
         datasets:
             - files: ['datasets/wine.csv', 'datasets/ionosphere.csv']
-              options: '-m 0 -M 20.3'
+              options: '-M 20.3'
 
     GMM:
         run: false
@@ -185,20 +185,20 @@
 library: matlab
 methods:
     PCA:
-        run: false
+        run: true
         script: methods/matlab/pca.py
         format: [csv, txt]
         datasets:
             - files: ['datasets/cities.csv', 'datasets/faces.csv', 'datasets/iris.csv' , 'datasets/wine.csv']
     NMF:
-        run: false
+        run: true
         script: methods/matlab/nmf.py
         format: [csv, txt]
         datasets:
             - files: ['datasets/piano_magnitude_spectogram.csv', 'datasets/wine.csv']
               options: '-r 6 -s 42 -u multdist'
-    KMeans:
-        run: false
+    KMEANS:
+        run: true
         script: methods/matlab/kmeans.py
         format: [csv, txt]
         datasets:
@@ -206,7 +206,7 @@
               options: '-c 3'
 
     NBC:
-        run: false
+        run: true
         script: methods/mlpack/nbc.py
         format: [csv, txt]
         datasets:
@@ -214,7 +214,7 @@
                        ['datasets/transfusion_train.csv', 'datasets/transfusion_test.csv'] ]
 
     ALLKNN:
-        run: false
+        run: true
         script: methods/mlpack/allknn.py
         format: [csv, txt]
         datasets:
@@ -222,7 +222,7 @@
               options: '-k 3 -s 42'
 
     RANGESEARCH:
-        run: false
+        run: true
         script: methods/mlpack/range_search.py
         format: [csv, txt]
         datasets:



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