def train(array, embedDim, interval):
XTrain, yTrain = pp.makeTrainset(array, embedDim, interval, 1)
kfold = cross_validation.KFold(len(XTrain), n_folds=5, shuffle=False)
params = {'n_estimators': randint(20, 200),
'loss': ['ls', 'lad', 'huber'],
'learning_rate': uniform(0.01, 0.19),
'subsample': uniform(0.5, 0.5),
'max_depth': randint(1, 5),
'min_samples_split': randint(1, 3),
'min_samples_leaf': randint(1, 3),
'max_features': randint(1, len(XTrain[0]))}
bestModels = []
for i in range(len(yTrain[0])):
gbrt = GradientBoostingRegressor()
clf = grid_search.RandomizedSearchCV(gbrt, param_distributions=params, n_iter=20,
scoring='mean_squared_error', cv=kfold, n_jobs=-1)
clf.fit(XTrain, yTrain[:, i])
bestModels.append(clf.best_estimator_)
for i in range(1, 12):
XTrain, yTrain = pp.makeTrainset(array, embedDim, interval, i) # 模型的预测天数递增
XPredict = pp.makeXPredict(array, embedDim, interval, i) # 待预测的输入递增
subyPredict = []
for j in range(len(yTrain[0])):
bestModels[j].fit(XTrain, yTrain[:, j])
subyPredict.append(bestModels[j].predict(XPredict))
array = np.hstack((array, np.array(copy(subyPredict)))) # 将一个模型的预测值作为已知数据，训练下一个模型
yPredict = array[0, -65:-5] # 一共可以预测66天，取其中对应的数据
return yPredict
def train(array, embedDim, interval):
XTrain, yTrain = pp.makeTrainset(array, embedDim, interval, 1)
kfold = cross_validation.KFold(len(XTrain), n_folds=5, shuffle=False)
params = {
'C': uniform(1, 99),
'gamma': uniform(0.01, 0.29),
'kernel': ['rbf', 'poly']
}
bestModels = []
for i in range(len(yTrain[0])):
svr = svm.SVR()
clf = grid_search.RandomizedSearchCV(svr,
param_distributions=params,
n_iter=30,
cv=kfold,
scoring='mean_squared_error',
n_jobs=1,
verbose=0)
clf.fit(XTrain, yTrain[:, i])
bestModels.append(clf.best_estimator_)
for i in range(1, 12):
XTrain, yTrain = pp.makeTrainset(array, embedDim, interval,
i) # 模型的预测天数递增
XPredict = pp.makeXPredict(array, embedDim, interval, i) # 待预测的输入递增
subyPredict = []
for j in range(len(yTrain[0])):
bestModels[j].fit(XTrain, yTrain[:, j])
subyPredict.append(bestModels[j].predict(XPredict))
array = np.hstack(
(array, np.array(copy(subyPredict)))) # 将一个模型的预测值作为已知数据，训练下一个模型
yPredict = array[0, -65:-5] # 一共可以预测66天，取其中对应的数据
return yPredict
def train(array, embedDim, interval):
XTrain, yTrain = pp.makeTrainset(array, embedDim, interval, 1)
kfold = cross_validation.KFold(len(XTrain), n_folds=4, shuffle=False)
params = {"n_estimators": randint(5, 100),
"max_depth": [1, 2, 3, 5, 8, 10, None],
"max_features": randint(1, len(XTrain[0])),
"min_samples_split": randint(1, 3),
"min_samples_leaf": randint(1, 3)}
bestModels = []
for i in range(len(yTrain[0])):
erf = ExtraTreesRegressor()
clf = grid_search.RandomizedSearchCV(erf, param_distributions=params, n_iter=10,
scoring='mean_squared_error', cv=kfold, n_jobs=-1)
clf.fit(XTrain, yTrain[:, i])
bestModels.append(clf.best_estimator_)
for i in range(60):
XTrain, yTrain = pp.makeTrainset(array, embedDim, interval, 1) # 模型的嵌入维度递增
XPredict = pp.makeXPredict(array, embedDim, interval, 1) # 待预测的嵌入维度递增
subyPredict = []
for j in range(len(yTrain[0])):
bestModels[j].fit(XTrain, yTrain[:, j])
subyPredict.append(bestModels[j].predict(XPredict))
array = np.hstack((array, np.array(copy(subyPredict)))) # 将一个模型的预测值作为已知数据，训练下一个模型
embedDim += 1
yPredict = array[0, -60:] # 一共可以预测60天，取其中对应的数据
return yPredict
def train(array, embedDim, interval):
distance = 7
XTrain, yTrain = pp.makeTrainset(array, embedDim, interval, distance)
kfold = cross_validation.KFold(len(XTrain), n_folds=5, shuffle=False)
params = {'n_estimators': randint(20, 200),
'loss': ['ls', 'lad', 'huber'],
'learning_rate': uniform(0.01, 0.19),
'subsample': uniform(0.5, 0.5),
'max_depth': randint(1, 5),
'min_samples_split': randint(1, 3),
'min_samples_leaf': randint(1, 3),
'max_features': randint(1, len(XTrain[0]))}
bestModels = []
for i in range(len(yTrain[0])):
gbrt = GradientBoostingRegressor()
clf = grid_search.RandomizedSearchCV(gbrt, param_distributions=params, n_iter=30,
scoring='mean_squared_error', cv=kfold, n_jobs=-1)
clf.fit(XTrain, yTrain[:, i])
bestModels.append(clf.best_estimator_)
for i in range(9):
XTrain, yTrain = pp.makeTrainset(array, embedDim, interval, distance) # 模型的嵌入维度递增
XPredict = pp.makeXPredict(array, embedDim, interval, distance) # 待预测的嵌入维度递增
subyPredict = []
for j in range(len(yTrain[0])):
bestModels[j].fit(XTrain, yTrain[:, j])
subyPredict.append(bestModels[j].predict(XPredict))
array = np.hstack((array, np.array(copy(subyPredict)))) # 将一个模型的预测值作为已知数据，训练下一个模型
embedDim += distance
yPredict = array[0, -62:-2] # 一共可以预测63天，取其中对应的数据
return yPredict
def train(array, embedDim, interval):
XTrain, yTrain = pp.makeTrainset(array, embedDim, interval, 1)
kfold = cross_validation.KFold(len(XTrain), n_folds=4, shuffle=False)
params = {
"n_estimators": randint(5, 100),
"max_depth": [1, 2, 3, 5, 8, 10, None],
"max_features": randint(1, len(XTrain[0])),
"min_samples_split": randint(1, 3),
"min_samples_leaf": randint(1, 3)
}
bestModels = []
for i in range(len(yTrain[0])):
erf = ExtraTreesRegressor()
clf = grid_search.RandomizedSearchCV(erf,
param_distributions=params,
n_iter=10,
scoring='mean_squared_error',
cv=kfold,
n_jobs=-1)
clf.fit(XTrain, yTrain[:, i])
bestModels.append(clf.best_estimator_)
for i in range(60):
XTrain, yTrain = pp.makeTrainset(array, embedDim, interval,
1) # 模型的嵌入维度递增
XPredict = pp.makeXPredict(array, embedDim, interval, 1) # 待预测的嵌入维度递增
subyPredict = []
for j in range(len(yTrain[0])):
bestModels[j].fit(XTrain, yTrain[:, j])
subyPredict.append(bestModels[j].predict(XPredict))
array = np.hstack(
(array, np.array(copy(subyPredict)))) # 将一个模型的预测值作为已知数据，训练下一个模型
embedDim += 1
yPredict = array[0, -60:] # 一共可以预测60天，取其中对应的数据
return yPredict
def train(array, embedDim, interval):
distance = 7
for i in range(9):
XTrain, yTrain = pp.makeTrainset(array, embedDim, interval,
distance) # 模型的预测天数
XPredict = pp.makeXPredict(array, embedDim, interval, distance)
params = {
'C': uniform(1, 99),
'gamma': uniform(0.01, 0.29),
'kernel': ['rbf', 'poly']
}
kfold = cross_validation.KFold(len(XTrain), n_folds=5, shuffle=False)
subyPredict = []
for j in range(len(yTrain[0])):
svr = svm.SVR()
clf = grid_search.RandomizedSearchCV(svr,
param_distributions=params,
n_iter=10,
cv=kfold,
scoring='mean_squared_error',
n_jobs=1,
verbose=0)
clf.fit(XTrain, yTrain[:, j])
subyPredict.append(clf.predict(XPredict))
array = np.hstack(
(array, np.array(copy(subyPredict)))) # 将一个模型的预测值作为已知数据，训练下一个模型
embedDim += distance
yPredict = array[0, -62:-2] # 一共可以预测63天，取其中对应的数据
return yPredict
def train(array, embedDim, interval):
distance=7
for i in range(9):
XTrain, yTrain = pp.makeTrainset(array, embedDim, interval, distance) # 模型的预测天数
XPredict = pp.makeXPredict(array, embedDim, interval, distance)
params = {'C': uniform(1, 99),
'gamma': uniform(0.01, 0.29),
'kernel': ['rbf', 'poly']}
kfold = cross_validation.KFold(len(XTrain), n_folds=5, shuffle=False)
subyPredict = []
for j in range(len(yTrain[0])):
svr = svm.SVR()
clf = grid_search.RandomizedSearchCV(svr, param_distributions=params, n_iter=10, cv=kfold,
scoring='mean_squared_error', n_jobs=1, verbose=0)
clf.fit(XTrain, yTrain[:, j])
subyPredict.append(clf.predict(XPredict))
array = np.hstack((array, np.array(copy(subyPredict)))) # 将一个模型的预测值作为已知数据，训练下一个模型
embedDim += distance
yPredict = array[0, -62:-2] # 一共可以预测63天，取其中对应的数据
return yPredict
def foldPredict(tracelist, embedDim, interval, distance):
clusterXPredict = []
for array in tracelist:
XPredict = pp.makeXPredict(array, embedDim, interval, distance)
clusterXPredict.append(copy(XPredict))
return clusterXPredict
def foldPredict(tracelist, embedDim, interval, distance):
clusterXPredict = []
for array in tracelist:
XPredict = pp.makeXPredict(array, embedDim, interval, distance)
clusterXPredict.append(copy(XPredict))
return clusterXPredict
