本文整理汇总了Python中pycast.common.timeseries.TimeSeries类的典型用法代码示例。如果您正苦于以下问题：Python TimeSeries类的具体用法？Python TimeSeries怎么用？Python TimeSeries使用的例子？那么恭喜您, 这里精选的类代码示例或许可以为您提供帮助。
在下文中一共展示了TimeSeries类的20个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于我们的系统推荐出更棒的Python代码示例。
示例1: smoothing_test
def smoothing_test(self):
"""Test smoothing part of ExponentialSmoothing."""
data = [[0, 10.0], [1, 18.0], [2, 29.0], [3, 15.0], [4, 30.0], [5, 30.0], [6, 12.0], [7, 16.0]]
tsSrc = TimeSeries.from_twodim_list(data)
tsSrc.normalize("second")
## Initialize a correct result.
### The numbers look a little bit odd, based on the binary translation problem
data = [[1.5, 10.0],[2.5, 12.4],[3.5, 17.380000000000003],[4.5, 16.666],[5.5, 20.6662],[6.5, 23.46634],[7.5, 20.026438]]
tsDst = TimeSeries.from_twodim_list(data)
## Initialize the method
es = ExponentialSmoothing(0.3, 0)
res = tsSrc.apply(es)
if not res == tsDst: raise AssertionError
data.append([8.5, 18.8185066])
tsDst = TimeSeries.from_twodim_list(data)
## Initialize the method
es = ExponentialSmoothing(0.3)
res = tsSrc.apply(es)
if not res == tsDst: raise AssertionError
开发者ID:fleupold，项目名称:pycast，代码行数:25，代码来源:methodtest.py
示例2: test_confidence_interval
def test_confidence_interval(self):
"""
Test if given two timeseries and a desired confidence interval,
regression gives us the correct over and underestimation.
"""
data_x = zip(range(100), range(100))
overestimations = [[90, 90 - 1], [91, 91 - 3], [92, 92 - 1], [93, 93 - 40], [94, 94 - 1]]
underestimations = [[95, 95 + 5], [96, 96 + 1 ], [97,97 + 4], [98, 98 + 3], [99, 99 + 1]]
data_y = data_x[:90] + overestimations + underestimations
ts_x = TimeSeries.from_twodim_list(data_x)
ts_y = TimeSeries.from_twodim_list(data_y)
#Mock the random.sample method so that we can use our outliers as samples
with patch('pycast.common.timeseries.random.sample') as sample_mock:
sample_mock.return_value = underestimations+overestimations
reg = Regression()
n, m, error = reg.calculate_parameters_with_confidence(ts_x, ts_y, .6)
#Since all values are the same the params should be n=0, m=1
self.assertEquals(0,n)
self.assertEquals(1,m)
#assert under/overestimation
self.assertEquals(error[0], -1)
self.assertEquals(error[1], 3)
开发者ID:T-002，项目名称:pycast，代码行数:27，代码来源:regressiontest.py
示例3: double_initialize_test
def double_initialize_test(self):
"""Test for the error ocuring when the same error measure is initialized twice."""
data = [[0.0, 0.0], [1, 0.1], [2, 0.2], [3, 0.3], [4, 0.4]]
tsOrg = TimeSeries.from_twodim_list(data)
tsCalc = TimeSeries.from_twodim_list(data)
bem = BaseErrorMeasure()
bem_calculate = bem._calculate
bem_local_error = bem.local_error
def return_zero(ignoreMe, ignoreMeToo):
return 0
## remove the NotImplementedErrors for initialization
bem.local_error = return_zero
bem._calculate = return_zero
## correct initialize call
bem.initialize(tsOrg, tsCalc)
## incorrect initialize call
for cnt in xrange(10):
try:
bem.initialize(tsOrg, tsCalc)
except StandardError:
pass
else:
assert False # pragma: no cover
bem.local_error = bem_calculate
bem._calculate = bem_local_error
开发者ID:744996162，项目名称:ali_match，代码行数:33，代码来源:errormeasuretest.py
示例4: start_and_enddate_test
def start_and_enddate_test(self):
"""Testing for startDate, endDate exceptions."""
data = [[0.0, 0.0], [1, 0.1], [2, 0.2], [3, 0.3], [4, 0.4]]
tsOrg = TimeSeries.from_twodim_list(data)
tsCalc = TimeSeries.from_twodim_list(data)
bem = MeanSquaredError()
bem.initialize(tsOrg, tsCalc)
for startDate in [0.0, 1, 2, 3]:
bem.get_error(startDate=startDate, endDate=4)
for endDate in [1, 2, 3, 4]:
bem.get_error(startDate=0.0, endDate=endDate)
try:
bem.get_error(startDate=23)
except ValueError:
pass
else:
assert False # pragma: no cover
try:
bem.get_error(endDate=-1)
except ValueError:
pass
else:
assert False # pragma: no cover
开发者ID:744996162，项目名称:ali_match，代码行数:28，代码来源:errormeasuretest.py
示例5: execute
def execute(self, timeSeries):
"""Creates a new TimeSeries containing the SMA values for the predefined windowsize.
:param TimeSeries timeSeries: The TimeSeries used to calculate the simple moving average values.
:return: TimeSeries object containing the smooth moving average.
:rtype: TimeSeries
:raise: Raises a :py:exc:`ValueError` wif the defined windowsize is larger than the number of elements
in timeSeries
:note: This implementation aims to support independent for loop execution.
"""
windowsize = self._parameters["windowsize"]
if len (timeSeries) < windowsize:
raise ValueError("windowsize is larger than the number of elements in timeSeries.")
tsLength = len(timeSeries)
nbrOfLoopRuns = tsLength - windowsize + 1
res = TimeSeries()
for idx in xrange(nbrOfLoopRuns):
end = idx + windowsize
data = timeSeries[idx:end]
timestamp = data[windowsize//2][0]
value = sum([i[1] for i in data])/windowsize
res.add_entry(timestamp, value)
res.sort_timeseries()
return res
开发者ID:T-002，项目名称:pycast，代码行数:33，代码来源:simplemovingaverage.py
示例6: calculate_parameters_one_empty_list_test
def calculate_parameters_one_empty_list_test(self):
"""Test for ValueError if one Timeseries are empty"""
tsOne = TimeSeries.from_twodim_list([[1, 12.34]])
tsTwo = TimeSeries.from_twodim_list([])
reg = Regression()
self.assertRaises(ValueError, reg.calculate_parameters, tsOne, tsTwo)
开发者ID:T-002，项目名称:pycast，代码行数:7，代码来源:regressiontest.py
示例7: initialization_test
def initialization_test(self):
"""Test for MASE initialization."""
dataOrg = [[1.0, 10], [2.0, 12], [3.0, 14], [4.0, 13], [5.0, 17], [6.0, 20], [7.0, 23], [8.0, 26], [9.0, 29], [10.0, 31], [11.0, 26], [12.0, 21], [13.0, 18], [14.0, 14], [15.0, 13], [16.0, 19], [17.0, 24], [18.0, 28], [19.0, 30], [20.0, 32]]
dataFor = [[1.0, 11], [2.0, 13], [3.0, 14], [4.0, 11], [5.0, 13], [6.0, 18], [7.0, 20], [8.0, 26], [9.0, 21], [10.0, 34], [11.0, 23], [12.0, 23], [13.0, 15], [14.0, 12], [15.0, 14], [16.0, 17], [17.0, 25], [18.0, 22], [19.0, 14], [20.0, 30]]
tsOrg = TimeSeries.from_twodim_list(dataOrg)
tsFor = TimeSeries.from_twodim_list(dataFor)
em = MeanAbsoluteScaledError(historyLength=5)
em.initialize(tsOrg, tsFor)
assert len(em._errorValues) == len(em._historicMeans), "For each error value an historic mean has to exsist."
try:
em.initialize(tsOrg, tsFor)
except StandardError:
pass
else:
assert False # pragma: no cover
em = MeanAbsoluteScaledError(historyLength=20.0)
em.initialize(tsOrg, tsFor)
assert len(em._errorValues) == len(em._historicMeans), "For each error value an historic mean has to exsist."
assert em._historyLength == 4, "The history is %s entries long. 4 were expected." % em._historyLength
em = MeanAbsoluteScaledError(historyLength=40.0)
em.initialize(tsOrg, tsFor)
assert len(em._errorValues) == len(em._historicMeans), "For each error value an historic mean has to exsist."
assert em._historyLength == 8, "The history is %s entries long. 8 were expected." % em._historyLength
开发者ID:744996162，项目名称:ali_match，代码行数:31，代码来源:errormeasuretest.py
示例8: train_target
def train_target(self, data_list, model_list):# data_list: [date, value]
orig = TimeSeries(isNormalized=True)
for i in range(len(data_list)):
orig.add_entry(data_list[i][0], data_list[i][1])
gridSearch = GridSearch(SMAPE)
optimal_forecasting, error, optimal_params = gridSearch.optimize(orig, model_list)
#print "======" + str(optimal_forecasting._parameters)
return optimal_forecasting
开发者ID:THUIR，项目名称:TianchiCompetition，代码行数:8，代码来源:pycast_predictor.py
示例9: timeseries___setitem___test
def timeseries___setitem___test(self):
"""Test TimeSeries.__setitem__"""
data = [[0.0, 0.0], [0.1, 0.1], [0.2, 0.2], [0.3, 0.3], [0.4, 0.4], [0.5, 0.5]]
tsOne = TimeSeries.from_twodim_list(data)
tsTwo = TimeSeries.from_twodim_list(data)
tsTwo[1] = [0.2, 0.4]
if tsOne == tsTwo: raise AssertionError
开发者ID:fleupold，项目名称:pycast，代码行数:8，代码来源:timeseriesmiscellaneoustest.py
示例10: energy_data
def energy_data(request):
"""
Connects to the database and loads Readings for device 8.
"""
cur = db.cursor().execute("""SELECT timestamp, current FROM Readings""")
original = TimeSeries()
original.initialize_from_sql_cursor(cur)
original.normalize("day", fusionMethod = "sum")
return Response(json.dumps(original, cls=PycastEncoder), content_type='application/json')
开发者ID:fleupold，项目名称:pycast，代码行数:9，代码来源:server.py
示例11: list_serialization_formatfree_test
def list_serialization_formatfree_test(self):
"""Test the format free list serialization."""
data = [[0.0, 0.0], [0.1, 0.1], [0.2, 0.2], [0.3, 0.3], [0.4, 0.4], [0.5, 0.5]]
tsOne = TimeSeries.from_twodim_list(data)
data = tsOne.to_twodim_list()
tsTwo = TimeSeries.from_twodim_list(data)
assert tsOne == tsTwo
开发者ID:fleupold，项目名称:pycast，代码行数:9，代码来源:timeseriesmiscellaneoustest.py
示例12: calculate_parameter_duplicate_dates_test
def calculate_parameter_duplicate_dates_test(self):
"""Test for ValueError if dates in timeseries are not distinct"""
# Initialize input
data1 = [[1, 12.23], [4, 23.34]]
data2 = [[1, 34.23], [1, 16.23]]
tsSrc1 = TimeSeries.from_twodim_list(data1)
tsSrc2 = TimeSeries.from_twodim_list(data2)
reg = Regression()
self.assertRaises(ValueError, reg.calculate_parameters, tsSrc1, tsSrc2)
开发者ID:T-002，项目名称:pycast，代码行数:10，代码来源:regressiontest.py
示例13: calculate_parameter_with_short_timeseries_test
def calculate_parameter_with_short_timeseries_test(self):
"""Test for ValueError if Timeseries has only one matching date"""
# Initialize input
data1 = [[1, 12.23], [4, 23.34]]
data2 = [[1, 34.23]]
tsSrc1 = TimeSeries.from_twodim_list(data1)
tsSrc2 = TimeSeries.from_twodim_list(data2)
reg = Regression()
self.assertRaises(ValueError, reg.calculate_parameters, tsSrc1, tsSrc2)
开发者ID:T-002，项目名称:pycast，代码行数:10，代码来源:regressiontest.py
示例14: timeseries_sort_test
def timeseries_sort_test(self):
"""Tests the sort_timeseries function."""
data = [[0.0, 0.0], [0.1, 0.1], [0.2, 0.2], [0.3, 0.3], [0.4, 0.4], [0.5, 0.5]]
ts = TimeSeries.from_twodim_list(data)
ts.sort_timeseries()
ts.sort_timeseries(False)
ts = TimeSeries(isSorted=True)
ts.sort_timeseries()
开发者ID:fleupold，项目名称:pycast，代码行数:10，代码来源:timeseriesmiscellaneoustest.py
示例15: list_serialization_format_test
def list_serialization_format_test(self):
"""Test the list serialization including time foramtting instructions."""
data = [[0.0, 0.0], [1.0, 0.1], [2.0, 0.2], [3.0, 0.3], [4.0, 0.4], [5.0, 0.5]]
tsOne = TimeSeries.from_twodim_list(data)
tsOne.set_timeformat("%Y-%m-%d_%H:%M:%S")
data = tsOne.to_twodim_list()
tsTwo = TimeSeries.from_twodim_list(data, format="%Y-%m-%d_%H:%M:%S")
assert tsOne == tsTwo
开发者ID:fleupold，项目名称:pycast，代码行数:10，代码来源:timeseriesmiscellaneoustest.py
示例16: calculate_parameters_without_match_test
def calculate_parameters_without_match_test(self):
"""Test for ValueError, if the input timeseries have no macthing dates"""
# Initialize input
data1 = [[1, 12.42], [6, 12.32], [8, 12.45]]
data2 = [[2, 32.45], [4, 23.12], [7, 65.34]]
tsOne = TimeSeries.from_twodim_list(data1)
tsTwo = TimeSeries.from_twodim_list(data2)
reg = Regression()
self.assertRaises(ValueError, reg.calculate_parameters, tsOne, tsTwo)
开发者ID:T-002，项目名称:pycast，代码行数:11，代码来源:regressiontest.py
示例17: forecasting_test
def forecasting_test(self):
data = [362.0, 385.0, 432.0, 341.0, 382.0, 409.0, 498.0, 387.0, 473.0, 513.0, 582.0, 474.0, 544.0, 582.0, 681.0, 557.0, 628.0, 707.0, 773.0, 592.0, 627.0, 725.0, 854.0, 661.0]
tsSrc = TimeSeries.from_twodim_list(zip(range(len(data)),data))
expected = [[0.0, 362.0],[1.0, 379.93673257607463],[2.0, 376.86173719924875],[3.0, 376.0203652542205],[4.0, 408.21988583215574],[5.0, 407.16235446485433],[6.0, 430.0950666716297],[7.0, 429.89797609228435],[8.0, 489.4888959723074],[9.0, 507.8407281475308],[10.0, 506.3556647249702],[11.0, 523.9422448655133],[12.0, 556.0311543025242],[13.0, 573.6520991970604],[14.0, 590.2149136780341],[15.0, 611.8813425659495],[16.0, 637.0393967524727],[17.0, 684.6600411792656],[18.0, 675.9589298142507],[19.0, 659.0266828674846],[20.0, 644.0903317144154],[21.0, 690.4507762388047],[22.0, 735.3219292023371],[23.0, 737.9752345691215],[24.0, 669.767091965978],[25.0, 737.5272444120604],[26.0, 805.3947787747426],[27.0, 902.1522777060334]]
hwm = HoltWintersMethod(.7556, 0.0000001, .9837, 4, valuesToForecast = 4)
res = tsSrc.apply(hwm)
#print res
assert len(res) == len(tsSrc) + 4
assert res == TimeSeries.from_twodim_list(expected)
开发者ID:fleupold，项目名称:pycast，代码行数:11，代码来源:methodtest.py
示例18: error_calculation_test
def error_calculation_test(self):
"""Testing for the correct MASE calculation.
History length is 5 in this test.
"""
dataOrg = [[1.0, 10], [2.0, 12], [3.0, 14], [4.0, 13], [5.0, 17], [6.0, 20], [7.0, 23], [8.0, 26], [9.0, 29], [10.0, 31], [11.0, 26], [12.0, 21], [13.0, 18], [14.0, 14], [15.0, 13], [16.0, 19], [17.0, 24], [18.0, 28], [19.0, 30], [20.0, 32]]
dataFor = [[1.0, 11], [2.0, 13], [3.0, 14], [4.0, 11], [5.0, 13], [6.0, 18], [7.0, 20], [8.0, 26], [9.0, 21], [10.0, 34], [11.0, 23], [12.0, 23], [13.0, 15], [14.0, 12], [15.0, 14], [16.0, 17], [17.0, 25], [18.0, 22], [19.0, 14], [20.0, 30]]
## 2 2 1 4 3 3 3 3 2 5 5 3 4 1 6 5 4 2 2
## Sum(History) 12 13 14 16 14 16 18 18 19 18 19 19 20 18 19
## Mean(History) ## ## ## ## ## 2.4 2.6 2.8 3.2 2.8 3.2 3.6 3.6 3.8 3.6 3.8 3.8 4.0 3.6 3.8
## AD 3 0 8 3 3 2 3 2 1 2 1 6 16 2
## Sum(AD) 3 3 11 14 17 19 22 24 25 27 28 34 50 52
## MAD 3 1.5 3.666 3.5 3.4 3.166 3.142 3 2.777 2.7 2.545 2.833 3.571 3.714
## MASE (0% - 100%) 1.25 0.625 1.527 1.458 1.416 1.319 1.309 1.25 1.157 1.125 1.06 1.18 1.602 1.547
tsOrg = TimeSeries.from_twodim_list(dataOrg)
tsFor = TimeSeries.from_twodim_list(dataFor)
historyLength = 5
em = MeanAbsoluteScaledError(historyLength=historyLength)
em.initialize(tsOrg, tsFor)
## check for error calculation depending on a specific endpoint
correctResult = [1.25, 0.625, 1.527, 1.458, 1.416, 1.319, 1.309, 1.25, 1.157, 1.125, "1.060", "1.180", 1.602, 1.547]
percentage = 100.0 / len(correctResult) + 0.2
for errVal in xrange(14):
endPercentage = percentage * (errVal + 1)
## set maximum percentage
if endPercentage > 100.0:
endPercentage = 100.0
calcErr = str(em.get_error(endPercentage=endPercentage))[:5]
correctRes = str(correctResult[errVal])[:5]
assert calcErr == correctRes
for errVal in xrange(14):
endDate = dataOrg[errVal + 6][0]
calcErr = str(em.get_error(endDate=endDate))[:5]
correctRes = str(correctResult[errVal])[:5]
assert calcErr == correctRes, "%s != %s" % (calcErr, correctRes)
em.get_error(startDate=7.0)
try:
em.get_error(startDate=42.23)
except ValueError:
pass
else:
assert False # pragma: no cover
开发者ID:744996162，项目名称:ali_match，代码行数:53，代码来源:errormeasuretest.py
示例19: check_normalization_test
def check_normalization_test(self):
"""Check for check_normalization."""
dataOK = zip(xrange(10), [random.random() for i in xrange(10)])
dataNotOK = dataOK[:]
del dataNotOK[2]
del dataNotOK[7]
tsOK = TimeSeries.from_twodim_list(dataOK)
tsNotOK = TimeSeries.from_twodim_list(dataNotOK)
assert tsOK._check_normalization()
assert not tsNotOK._check_normalization()
开发者ID:T-002，项目名称:pycast，代码行数:12，代码来源:timeseriesmiscellaneoustest.py
示例20: normalize_test
def normalize_test(self):
"""Test timeseries normalization."""
dataOne = [[0.0, 0.0], [1.0, 1.0], [2.0, 2.0], [5.1, 5.0]]
dataTwo = [[0.5, 0.0], [1.5, 1.0], [2.5, 2.0], [3.5, 3.0], [4.5, 4.0], [5.5, 5.0]]
tsOne = TimeSeries.from_twodim_list(dataOne)
tsTwo = TimeSeries.from_twodim_list(dataTwo)
tsOne.normalize("second")
if not len(tsOne) == len(tsTwo): raise AssertionError
if not tsOne == tsTwo: raise AssertionError
开发者ID:fleupold，项目名称:pycast，代码行数:12，代码来源:timeseriesmiscellaneoustest.py
注：本文中的pycast.common.timeseries.TimeSeries类示例由纯净天空整理自Github/MSDocs等源码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。 |
客服电话
APP下载
官方微信
请发表评论