Changed plot so that data0=empty is possible.

library/exercise.py

@@ -223,11 +223,19 @@ def plotCloud(data0, data1, dataNew=None, outputFile=None, title="", doShow=True
     Does a PCA analysis of the given data and plot the both important axis.
     """
 
+    if data0.shape[0] > 0:
+        if data1.shape[0] > 0:
+            data = np.concatenate([data0, data1])
+        else:
+            data = data0
+    else:
+        data = data1
+
     # Normalizes the data.
     if dataNew is None:
-        data_t = StandardScaler().fit_transform(np.concatenate([data0, data1]))
+        data_t = StandardScaler().fit_transform(data)
     else:
-        data_t = StandardScaler().fit_transform(np.concatenate([data0, data1, dataNew]))
+        data_t = StandardScaler().fit_transform(np.concatenate([data, dataNew]))
 
 
     # Run the PCA analysis.
@@ -248,7 +256,12 @@ def plotCloud(data0, data1, dataNew=None, outputFile=None, title="", doShow=True
 
     m = 0
     n = len(data0)
-    doSubplot(m, n, "gray")
+    labels = []
+    if n > 0:
+        labels = ["majority", "minority"]
+        doSubplot(m, n, "gray")
+    else:
+        labels = ["data"]
     
     m += n
     n = len(data1)
@@ -257,9 +270,10 @@ def plotCloud(data0, data1, dataNew=None, outputFile=None, title="", doShow=True
     if dataNew is not None:
         m += n
         n = len(dataNew)
+        labels.append("synthetic")
         doSubplot(m, n, "blue")
 
-    ax.legend(title="", loc='upper left', labels=['majority', 'minority', 'synthetic minority'])
+    ax.legend(title="", loc='upper left', labels=labels)
     ax.set_xlabel("PCA0")
     ax.set_ylabel("PCA1")
     if doShow: