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authorDavid Luevano <55825613+luevano@users.noreply.github.com>2019-12-28 11:12:36 -0700
committerDavid Luevano <55825613+luevano@users.noreply.github.com>2019-12-28 11:12:36 -0700
commite4f9e15588ec796f73c000a683cc9152454a913c (patch)
tree698f24d36cbf851e263625adb883339efaaacdce /lj_matrix
parentc1e7b327655ebaa5c44e4bef5b9b675b23782952 (diff)
Fix bugs
Diffstat (limited to 'lj_matrix')
-rw-r--r--lj_matrix/__main__.py10
-rw-r--r--lj_matrix/do_ml.py7
2 files changed, 14 insertions, 3 deletions
diff --git a/lj_matrix/__main__.py b/lj_matrix/__main__.py
index 98f341e1e..811024ff0 100644
--- a/lj_matrix/__main__.py
+++ b/lj_matrix/__main__.py
@@ -24,6 +24,14 @@ from lj_matrix.do_ml import do_ml
# from lj_matrix.misc import plot_benchmarks
if __name__ == '__main__':
- do_ml(min_training_size=1500, max_training_size=3000)
+ do_ml(min_training_size=1500,
+ max_training_size=2000,
+ training_increment_size=500,
+ test_size=None,
+ ljm_diag_value=None,
+ ljm_sigma=1.0,
+ ljm_epsilon=1.0,
+ save_benchmarks=False,
+ show_msgs=True)
# plot_benchmarks()
print('OK!')
diff --git a/lj_matrix/do_ml.py b/lj_matrix/do_ml.py
index 45dc7a5f0..da9386bf7 100644
--- a/lj_matrix/do_ml.py
+++ b/lj_matrix/do_ml.py
@@ -114,6 +114,7 @@ def ml(desc_data,
def do_ml(min_training_size,
max_training_size=None,
training_increment_size=500,
+ test_size=None,
ljm_diag_value=None,
ljm_sigma=1.0,
ljm_epsilon=1.0,
@@ -128,6 +129,8 @@ def do_ml(min_training_size,
min_training_size: minimum training size.
max_training_size: maximum training size.
training_increment_size: training increment size.
+ test_size: size of the test set to use. If no size is given,
+ the last remaining molecules are used.
ljm_diag_value: if a special diagonal value should be used in lj matrix.
ljm_sigma: sigma value for lj matrix.
ljm_epsilon: epsilon value for lj matrix.
@@ -170,7 +173,7 @@ def do_ml(min_training_size,
i,
'CM',
cm_send,
- max_training_size,
+ test_size,
sigma,
show_msgs))
procs.append(p1)
@@ -184,7 +187,7 @@ def do_ml(min_training_size,
i,
'L-JM',
ljm_send,
- max_training_size,
+ test_size,
sigma,
show_msgs))
procs.append(p2)