fixed retrieval of conditions from kwargs

previously samples used to check if c in self and that would pass the unit test 
The output would be a graph with no plots but a legend with non existent conditions
The correct output should have no conditions in the legend for non existent keys
Hence in the case of conditions being supplied through kwargs it need to be checked with self(conditions exist in self or not)

nltk/probability.py

@@ -1913,46 +1913,46 @@ def plot(self, *args, **kwargs):
 
         cumulative = _get_kwarg(kwargs, 'cumulative', False)
         percents = _get_kwarg(kwargs, 'percents', False)
-        conditions = _get_kwarg(kwargs, 'conditions', sorted(self.conditions()))
+        conditions = [c for c in _get_kwarg(kwargs, 'conditions', self.conditions()) if c in self] # conditions should be in self
         title = _get_kwarg(kwargs, 'title', '')
         samples = _get_kwarg(
-            kwargs, 'samples', sorted(set(v for c in conditions
-                                          if c in self
-                                          for v in self[c]))
+            kwargs, 'samples', sorted(set(v 
+                                            for c in conditions
+                                            for v in self[c]))
         )  # this computation could be wasted
         if "linewidth" not in kwargs:
             kwargs["linewidth"] = 2
-        freqs = []
-        for condition in conditions:
-            if cumulative:
-                # freqs should be a list of list where each sub list will be a frequency of a condition
-                freqs.append(list(self[condition]._cumulative_frequencies(samples)))
-                ylabel = "Cumulative Counts"
-                legend_loc = 'lower right'
-                if percents:
-                    freqs[-1] = [f / freqs[len(freqs) - 1] * 100 for f in freqs]
-                    ylabel = "Cumulative Percents"
-            else:
-                freqs.append([self[condition][sample] for sample in samples])
-                ylabel = "Counts"
-                legend_loc = 'upper right'
-            # percents = [f * 100 for f in freqs] only in ConditionalProbDist?
-
-
         ax = plt.gca()
-        i = 0
-        for freq in freqs:
-            kwargs['label'] = conditions[i] #label for each condition
-            i += 1
-            ax.plot(freq, *args, **kwargs)
-        ax.legend(loc=legend_loc)
-        ax.grid(True, color="silver")
-        ax.set_xticks(range(len(samples)))
-        ax.set_xticklabels([text_type(s) for s in samples], rotation=90)
-        if title:
-            ax.set_title(title)
-        ax.set_xlabel("Samples")
-        ax.set_ylabel(ylabel)
+        if (len(conditions) != 0):
+            freqs = []
+            for condition in conditions:
+                if cumulative:
+                    # freqs should be a list of list where each sub list will be a frequency of a condition
+                    freqs.append(list(self[condition]._cumulative_frequencies(samples)))
+                    ylabel = "Cumulative Counts"
+                    legend_loc = 'lower right'
+                    if percents:
+                        freqs[-1] = [f / freqs[len(freqs) - 1] * 100 for f in freqs]
+                        ylabel = "Cumulative Percents"
+                else:
+                    freqs.append([self[condition][sample] for sample in samples])
+                    ylabel = "Counts"
+                    legend_loc = 'upper right'
+                # percents = [f * 100 for f in freqs] only in ConditionalProbDist?
+
+            i = 0
+            for freq in freqs:
+                kwargs['label'] = conditions[i] #label for each condition
+                i += 1
+                ax.plot(freq, *args, **kwargs)
+            ax.legend(loc=legend_loc)
+            ax.grid(True, color="silver")
+            ax.set_xticks(range(len(samples)))
+            ax.set_xticklabels([text_type(s) for s in samples], rotation=90)
+            if title:
+                ax.set_title(title)
+            ax.set_xlabel("Samples")
+            ax.set_ylabel(ylabel)
         plt.show()
 
         return ax