Add histogram fig

1 files changed, 6 insertions, 1 deletions

diff --git a/evaluate.py b/evaluate.py
index dff8482..9cb5f78 100755
--- a/evaluate.py
+++ b/evaluate.py
@@ -19,7 +19,7 @@ import time
 parser = argparse.ArgumentParser()
 parser.add_argument("-d", "--data", help="Data path", action='store_true', default='data.npz')
 parser.add_argument("-c", "--conf_mat", help="Show visual confusion matrix", action='store_true')
-parser.add_argument("-k", "--kmean", help="Perform kmean clustering with --kmean cluster centers", type=int, default=0)
+parser.add_argument("-k", "--kmean", help="Perform kmean clustering with KMEAN cluster centers", type=int, default=0)
 parser.add_argument("-l", "--leaves", help="Maximum leaf nodes for RF classifier", type=int, default=256)
 parser.add_argument("-e", "--estimators", help="number of estimators to be used", type=int, default=100)
 parser.add_argument("-D", "--treedepth", help="depth of trees", type=int, default=5)
@@ -49,6 +49,11 @@ def make_histogram(data, model, args):
                 leaves = model.apply(data[i][j].T)
                 leaves = np.apply_along_axis(np.bincount, axis=0, arr=leaves, minlength=args.leaves)
                 histogram[i][j] = leaves.reshape(hist_size)
+
+    print(histogram[0][0].shape)
+    plt.bar(np.arange(100), histogram[0][0].flatten())
+    plt.show()
+
     return histogram
 
 def run_model (data, train, test, train_part, args):