Fix kcross validation
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2 changed files with 11 additions and 19 deletions
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@ -24,15 +24,8 @@ def split(data, k):
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bucketsize = int(len(samples) / k)
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bucketsize = int(len(samples) / k)
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print(k)
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print(len(data))
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print(len(samples))
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print(bucketsize)
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# K steps
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# K steps
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for i in range(k):
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for i in range(k):
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eval_dict = []
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train_dict = []
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eval_samples = []
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eval_samples = []
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train_samples = []
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train_samples = []
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for j in range(k):
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for j in range(k):
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@ -41,19 +34,18 @@ def split(data, k):
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else:
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else:
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train_samples.extend(samples[i*bucketsize:(i+1)*bucketsize])
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train_samples.extend(samples[i*bucketsize:(i+1)*bucketsize])
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for s in eval_samples:
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# Create new dictionaries in the eval lists
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# Create new dictionaries in the eval lists
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X_eval.append({'dc': eval_samples[:-1]})
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X_eval.append({'dc': s[:-1]})
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y_eval.append({'dc': eval_samples[-1]})
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y_eval.append({'dc': s[-1]})
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for s in train_samples:
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X_train.append({'dc': train_samples[:-1]})
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X_train.append({'dc': s[:-1]})
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y_train.append({'dc': train_samples[-1]})
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y_train.append({'dc': s[-1]})
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print(len(X_train) / 12)
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print(len(X_eval))
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#print(X_train)
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print(len(y_eval))
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#print(y_train)
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exit(0)
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print(len(X_train))
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print(len(y_train))
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return X_train, y_train, X_eval, y_eval
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return X_train, y_train, X_eval, y_eval
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@ -25,7 +25,7 @@ n = pywatts.neural.Net(feature_cols=feature_col)
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(X_train, y_train, X_eval, y_eval) = kcross.split(df, K)
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(X_train, y_train, X_eval, y_eval) = kcross.split(df, K)
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train_eval = {}
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#train_eval = {}
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if TRAIN:
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if TRAIN:
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# Train the model with the steps given
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# Train the model with the steps given
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