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Fixed (?) kcross

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This commit is contained in:
reedts 2018-08-09 11:54:33 +02:00
parent 2dfe5ef1b6
commit 288be08699
2 changed files with 23 additions and 15 deletions
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2
pywatts/db.py
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@ -6,7 +6,7 @@ from playhouse.sqlite_ext import SqliteExtDatabase
import os.path
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
db_path = os.path.join(BASE_DIR, "../pywatts.db")
db_path = os.path.join(BASE_DIR, "pywatts.db")
print(db_path)
db = SqliteExtDatabase(db_path)

36
pywatts/kcross.py
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@ -30,22 +30,32 @@ def split(data, k):
train_samples = []
for j in range(k):
if j == i:
eval_samples.extend(samples[i*bucketsize:(i+1)*bucketsize])
eval_samples.extend(samples[j*bucketsize:(j+1)*bucketsize])
else:
train_samples.extend(samples[i*bucketsize:(i+1)*bucketsize])
train_samples.extend(samples[j*bucketsize:(j+1)*bucketsize])
# Create new dictionaries in the eval lists
X_eval.append({'dc': eval_samples[:-1]})
y_eval.append({'dc': eval_samples[-1]})
#X_eval.append({'dc': eval_samples[:-1]})
#y_eval.append({'dc': eval_samples[-1]})
X_eval.append({'dc': [x for s in eval_samples for c, x in enumerate(s, 1) if c % 337 != 0]})
y_eval.append({'dc': [x for s in eval_samples for c, x in enumerate(s, 1) if c % 337 == 0]})
X_train.append({'dc': train_samples[:-1]})
y_train.append({'dc': train_samples[-1]})
#X_train.append({'dc': train_samples[:-1]})
#y_train.append({'dc': train_samples[-1]})
X_train.append({'dc': [x for s in train_samples for c, x in enumerate(s, 1) if c % 337 != 0]})
y_train.append({'dc': [x for s in train_samples for c, x in enumerate(s, 1) if c % 337 == 0]})
print(len(X_eval))
print(len(y_eval))
print(len(X_train))
print(len(y_train))
#print(len(X_eval))
#print(len(y_eval))
#print(len(X_train))
#print(len(y_train))
#print(len(X_train[0]['dc']))
#print(len(y_train[0]['dc']))
#print(len(X_eval[0]['dc']))
#print(len(y_eval[0]['dc']))
#print(X_train)
#print(y_train)
#exit(0)
return X_train, y_train, X_eval, y_eval
@ -56,12 +66,10 @@ def train(nn, X_train, y_train, X_eval, y_eval, steps=10):
for count, train_data in enumerate(X_train):
for i in range(steps):
nn.train(train_data, y_train[count], batch_size=int(len(train_data['dc'])/336), steps=1)
print(X_eval[count])
print(len(X_eval[count]['dc']))
print(y_eval[count])
evaluation.append(nn.evaluate(X_eval[count], y_eval[count], batch_size=int(len(X_eval[count]['dc'])/336)))
print("Training %s: %s/%s" % (count, (i+1), steps))
return evaluation