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authornunzip <np.scarh@gmail.com>2019-02-06 21:28:51 +0000
committernunzip <np.scarh@gmail.com>2019-02-06 21:28:51 +0000
commit13d71482da52bb3ca7c56a0054e8d151a441aee0 (patch)
tree6b5d407854f90f7d9e8d7c40f49c5b89a3e0e71b
parent9c7644482af96b6462d6e8049d93c9f46cf29def (diff)
parentc9220a2981d3f2b2f6dfc8d51834801b604c9e7d (diff)
downloade4-vision-13d71482da52bb3ca7c56a0054e8d151a441aee0.tar.gz
e4-vision-13d71482da52bb3ca7c56a0054e8d151a441aee0.tar.bz2
e4-vision-13d71482da52bb3ca7c56a0054e8d151a441aee0.zip
Fixed merge conflicts
-rwxr-xr-xevaluate.py7
1 files changed, 4 insertions, 3 deletions
diff --git a/evaluate.py b/evaluate.py
index 2fb805d..d4d0b07 100755
--- a/evaluate.py
+++ b/evaluate.py
@@ -28,6 +28,7 @@ parser.add_argument("-t", "--timer", help="Display execution time", action='stor
parser.add_argument("-T", "--testmode", help="Testmode", action='store_true')
parser.add_argument("-E", "--embest", help="RandomTreesEmbedding estimators", type=int, default=256)
parser.add_argument("-r", "--randomness", help="Randomness parameter", type=int, default=0)
+parser.add_argument("-s", "--seed", help="Seed to use for random_state when creating trees", type=int, default=0)
args = parser.parse_args()
if args.verbose:
@@ -56,11 +57,11 @@ def run_model (data, train, test, train_part, args):
if (args.kmean):
logging.debug("Computing KMeans with", train_part.shape[0], "keywords")
- kmeans = KMeans(n_clusters=args.kmean, n_init=1, random_state=0).fit(train_part)
+ kmeans = KMeans(n_clusters=args.kmean, n_init=1, random_state=args.seed).fit(train_part)
hist_train = make_histogram(train, kmeans, args)
hist_test = make_histogram(test, kmeans, args)
else:
- trees = RandomTreesEmbedding(max_leaf_nodes=args.leaves, n_estimators=args.embest, random_state=0).fit(train_part)
+ trees = RandomTreesEmbedding(max_leaf_nodes=args.leaves, n_estimators=args.embest, random_state=args.seed).fit(train_part)
hist_train = make_histogram(train, trees, args)
hist_test = make_histogram(test, trees, args)
@@ -71,7 +72,7 @@ def run_model (data, train, test, train_part, args):
if args.randomness:
clf = RandomForestClassifier(max_features=args.randomness, n_estimators=args.estimators, max_depth=args.treedepth, random_state=0)
else:
- clf = RandomForestClassifier(n_estimators=args.estimators, max_depth=args.treedepth, random_state=0)
+ clf = RandomForestClassifier(n_estimators=args.estimators, max_depth=args.treedepth, random_state=args.seed)
clf.fit(
hist_train.reshape((hist_train.shape[0]*hist_train.shape[1], hist_train.shape[2])),
np.repeat(np.arange(hist_train.shape[0]), hist_train.shape[1]))