|
@@ -27,7 +27,7 @@ from tensorflow.keras.layers import Lambda
|
|
|
|
|
|
|
|
import time
|
|
import time
|
|
|
|
|
|
|
|
-from library.NNSearch import NNSearch
|
|
|
|
|
|
|
+from library.NNSearch_experimental import NNSearch
|
|
|
from library.timing import timing
|
|
from library.timing import timing
|
|
|
|
|
|
|
|
import warnings
|
|
import warnings
|
|
@@ -43,7 +43,7 @@ def create01Labels(totalSize, sizeFirstHalf):
|
|
|
labels.extend(repeat(np.array([0,1]), totalSize - sizeFirstHalf))
|
|
labels.extend(repeat(np.array([0,1]), totalSize - sizeFirstHalf))
|
|
|
return np.array(labels)
|
|
return np.array(labels)
|
|
|
|
|
|
|
|
-class ConvGAN2(GanBaseClass):
|
|
|
|
|
|
|
+class ConvGAN_experimental(GanBaseClass):
|
|
|
"""
|
|
"""
|
|
|
This is a toy example of a GAN.
|
|
This is a toy example of a GAN.
|
|
|
It repeats the first point of the training-data-set.
|
|
It repeats the first point of the training-data-set.
|
|
@@ -378,7 +378,7 @@ class ConvGAN2(GanBaseClass):
|
|
|
self.timing["NMB"].start()
|
|
self.timing["NMB"].start()
|
|
|
t = time.time()
|
|
t = time.time()
|
|
|
neigh = NNSearch(self.neb, timingDict=self.timing)
|
|
neigh = NNSearch(self.neb, timingDict=self.timing)
|
|
|
- neigh.fit(data_min)
|
|
|
|
|
|
|
+ neigh.fit_cLib(data_min)
|
|
|
self.tNbhFit += (time.time() - t)
|
|
self.tNbhFit += (time.time() - t)
|
|
|
self.nNbhFit += 1
|
|
self.nNbhFit += 1
|
|
|
self.timing["NMB"].stop()
|
|
self.timing["NMB"].stop()
|
