|
|
|
@ -584,7 +584,7 @@ class Predict(GNet):
|
|
|
|
|
p = 0 not in df.sum(axis=1).values
|
|
|
|
|
x = df.sum(axis=1).values
|
|
|
|
|
|
|
|
|
|
if np.divide( np.sum(x), x.size) > .9 or p and np.sum(x) == x.size:
|
|
|
|
|
if x.max() == 1 and np.divide( np.sum(x), x.size) > .9 or p and np.sum(x) == x.size and x.size == self.values.size:
|
|
|
|
|
ratio.append(np.divide( np.sum(x), x.size))
|
|
|
|
|
found.append(df)
|
|
|
|
|
if i == CANDIDATE_COUNT:
|
|
|
|
@ -606,7 +606,9 @@ class Predict(GNet):
|
|
|
|
|
|
|
|
|
|
# r = np.zeros((self.ROW_COUNT,len(columns)))
|
|
|
|
|
# r = np.zeros(self.ROW_COUNT)
|
|
|
|
|
|
|
|
|
|
if self.logger :
|
|
|
|
|
info = {"found":len(found),"selected":INDEX, "ratio": ratio[INDEX],"rows":df.shape[0],"cols":df.shape[1]}
|
|
|
|
|
self.logger.write({"module":"gan-generate","action":"generate","input":info})
|
|
|
|
|
df.columns = self.values
|
|
|
|
|
if len(found):
|
|
|
|
|
# print (len(found),NTH_VALID_CANDIDATE)
|
|
|
|
|