Merge pull request #2580 from iglesias/fix_warnings

Fix CARTree warning.
shogun-toolbox · Oct 28, 2014 · 44aea24 · 44aea24
2 parents ee8a8d2 + 0bb5557
commit 44aea24
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Showing 6 changed files with 16 additions and 16 deletions.
diff --git a/doc/ipython-notebooks/multiclass/Tree/DecisionTrees.ipynb b/doc/ipython-notebooks/multiclass/Tree/DecisionTrees.ipynb
@@ -1051,9 +1051,9 @@
       "from modshogun import PT_MULTICLASS, CARTree\n",
       "from numpy import array\n",
       "\n",
-      "def train_carttree(feat_types,num_folds,problem_type,use_cv_pruning,labels,features):\n",
+      "def train_carttree(feat_types,problem_type,num_folds,use_cv_pruning,labels,features):\n",
       "    # create CART tree object\n",
-      "    c = CARTree(feat_types,num_folds,problem_type,use_cv_pruning)\n",
+      "    c = CARTree(feat_types,problem_type,num_folds,use_cv_pruning)\n",
       "    # set training labels\n",
       "    c.set_labels(labels)\n",
       "    # train using training features\n",
@@ -1065,7 +1065,7 @@
       "ft = array([True, False])\n",
       "\n",
       "# get back trained tree\n",
-      "cart = train_carttree(ft, 5, PT_MULTICLASS, True, train_labels, train_feats)"
+      "cart = train_carttree(ft, PT_MULTICLASS, 5, True, train_labels, train_feats)"
      ],
      "language": "python",
      "metadata": {},
@@ -1177,7 +1177,7 @@
       "feat_type = array([False])\n",
       "\n",
       "# get back trained tree\n",
-      "cart = train_carttree(feat_type, 5, PT_REGRESSION, True, train_labels, train_feats)"
+      "cart = train_carttree(feat_type, PT_REGRESSION, 5, True, train_labels, train_feats)"
      ],
      "language": "python",
      "metadata": {},
@@ -1292,7 +1292,7 @@
       "feature_types = array([False, False, False, False])\n",
       "\n",
       "# setup CART-tree with cross validation pruning switched off\n",
-      "cart = CARTree(feature_types,5,PT_MULTICLASS,False)"
+      "cart = CARTree(feature_types,PT_MULTICLASS,5,False)"
      ],
      "language": "python",
      "metadata": {},
@@ -1409,7 +1409,7 @@
       "    # set attribute types - 2 nominal and 2 ordinal\n",
       "    feature_types = array([True, True, False, False])\n",
       "    # setup CART-tree with cross validation pruning switched off\n",
-      "    cart = CARTree(feature_types,5,PT_REGRESSION,False)\n",
+      "    cart = CARTree(feature_types,PT_REGRESSION,5,False)\n",
       "    # set max allowed depth\n",
       "    cart.set_max_depth(max_depth)\n",
       "\n",
@@ -1846,4 +1846,4 @@
    "metadata": {}
   }
  ]
-}
+}
diff --git a/doc/ipython-notebooks/multiclass/Tree/TreeEnsemble.ipynb b/doc/ipython-notebooks/multiclass/Tree/TreeEnsemble.ipynb
@@ -150,7 +150,7 @@
       "from modshogun import CARTree, PT_MULTICLASS\n",
       "\n",
       "def train_cart(train_feats,train_labels,feature_types,problem_type):\n",
-      "    c=CARTree(feature_types,2,problem_type,False)\n",
+      "    c=CARTree(feature_types,problem_type,2,False)\n",
       "    c.set_labels(train_labels)\n",
       "    c.train(train_feats)\n",
       "    \n",
@@ -514,4 +514,4 @@
    "metadata": {}
   }
  ]
-}
+}
diff --git a/examples/undocumented/python_modular/multiclass_cartree_modular.py b/examples/undocumented/python_modular/multiclass_cartree_modular.py
@@ -23,7 +23,7 @@ def multiclass_cartree_modular(train=traindat,test=testdat,labels=label_traindat
 	train_labels=MulticlassLabels(CSVFile(labels))
 
 	# CART Tree formation with 5 fold cross-validation pruning
-	c=CARTree(ft,5,PT_MULTICLASS,True)
+	c=CARTree(ft,PT_MULTICLASS,5,True)
 	c.set_labels(train_labels)
 	c.train(feats_train)
 

diff --git a/examples/undocumented/python_modular/regression_cartree_modular.py b/examples/undocumented/python_modular/regression_cartree_modular.py
@@ -29,7 +29,7 @@ def regression_cartree_modular(num_train=500,num_test=50,x_range=15,noise_var=0.
 	train_labels=RegressionLabels(Y_train[0])
 
 	# CART Tree formation
-	c=CARTree(ft,5,PT_REGRESSION,True)
+	c=CARTree(ft,PT_REGRESSION,5,True)
 	c.set_labels(train_labels)
 	c.train(feats_train)
 

diff --git a/src/shogun/multiclass/tree/CARTree.cpp b/src/shogun/multiclass/tree/CARTree.cpp
@@ -51,7 +51,7 @@ CCARTree::CCARTree(SGVector<bool> attribute_types, EProblemType prob_type)
 	set_machine_problem_type(prob_type);
 }
 
-CCARTree::CCARTree(SGVector<bool> attribute_types, int32_t num_folds, EProblemType prob_type, bool cv_prune)
+CCARTree::CCARTree(SGVector<bool> attribute_types, EProblemType prob_type, int32_t num_folds, bool cv_prune)
 : CTreeMachine<CARTreeNodeData>()
 {
 	init();

diff --git a/src/shogun/multiclass/tree/CARTree.h b/src/shogun/multiclass/tree/CARTree.h
@@ -89,12 +89,12 @@ class CCARTree : public CTreeMachine<CARTreeNodeData>
 	CCARTree(SGVector<bool> attribute_types, EProblemType prob_type=PT_MULTICLASS);
 
 	/** constructor - to be used while using cross-validation pruning
-	 * @param attribute_types type of each predictive attribute (true for nominal, false for ordinal/continuous) 
+	 * @param attribute_types type of each predictive attribute (true for nominal, false for ordinal/continuous)
+	 * @param prob_type machine problem type - PT_MULTICLASS or PT_REGRESSION
 	 * @param num_folds number of subsets used in cross-valiation
-	 * @param prob_type machine problem type - PT_MULTICLASS or PT_REGRESSION 
-	 * @param cv_prune - whether to use cross-validation pruning, True by default
+	 * @param cv_prune - whether to use cross-validation pruning
 	 */
-	CCARTree(SGVector<bool> attribute_types, int32_t num_folds, EProblemType prob_type=PT_MULTICLASS, bool cv_prune=true);
+	CCARTree(SGVector<bool> attribute_types, EProblemType prob_type, int32_t num_folds, bool cv_prune);
 
 	/** destructor */
 	virtual ~CCARTree();