本文整理汇总了Python中Feature.FeatureSpace类的典型用法代码示例。如果您正苦于以下问题：Python FeatureSpace类的具体用法？Python FeatureSpace怎么用？Python FeatureSpace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FeatureSpace类的15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，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_Period_Psi
def test_Period_Psi(fake_lc):
# data, mjd, error, second_data, aligned_data, aligned_second_data, aligned_mjd = fake_lc()
a = FeatureSpace(featureList=['PeriodLS', 'Period_fit','Psi_CS','Psi_eta'], PeriodLS = mjd, Psi_CS= mjd)
a=a.calculateFeature(fake_lc[0])
assert(a.result(method='array') >= 0.043 and a.result(method='array') <= 0.046)示例2: test_PairSlopeTrend
def test_PairSlopeTrend(white_noise):
# data, mjd, error, second_data, aligned_data, aligned_second_data, aligned_mjd = white_noise()
a = FeatureSpace(featureList=['PairSlopeTrend'])
a=a.calculateFeature(white_noise)
assert(a.result(method='array') >= -0.25 and a.result(method='array') <= 0.25)示例3: test_MedianBRP
def test_MedianBRP(fake_lc):
# data, mjd, error, second_data, aligned_data, aligned_second_data, aligned_mjd = fake_lc()
a = FeatureSpace(featureList=['MedianBRP'] , MaxSlope=mjd)
a=a.calculateFeature(fake_lc[0])
assert(a.result(method='array') >= 0.043 and a.result(method='array') <= 0.046)示例4: test_Meanvariance
def test_Meanvariance(uniform_lc):
# data, mjd, error, second_data, aligned_data, aligned_second_data, aligned_mjd = white_noise()
a = FeatureSpace(featureList=['Meanvariance'])
a=a.calculateFeature(uniform_lc)
assert(a.result(method='array') >= 0.575 and a.result(method='array') <= 0.580)示例5: test_MedianAbsDev
def test_MedianAbsDev(white_noise):
# data, mjd, error, second_data, aligned_data, aligned_second_data, aligned_mjd = white_noise()
a = FeatureSpace(featureList=['MedianAbsDev'])
a=a.calculateFeature(white_noise)
assert(a.result(method='array') >= 0.630 and a.result(method='array') <= 0.700)示例6: test_Eta_e
def test_Eta_e(white_noise):
# data, mjd, error, second_data, aligned_data, aligned_second_data, aligned_mjd = white_noise()
a = FeatureSpace(featureList=['Eta_e'])
a=a.calculateFeature(white_noise)
assert(a.result(method='array') >= 1.9 and a.result(method='array') <= 2.1)示例7: test_Eta_e
def test_Eta_e(fake_lc):
# data, mjd, error, second_data, aligned_data, aligned_second_data, aligned_mjd = fake_lc()
a = FeatureSpace(featureList=['Eta_e'] )
a=a.calculateFeature(fake_lc[0])
assert(a.result(method='array') >= 0.043 and a.result(method='array') <= 0.046)示例8: test_Con
def test_Con(white_noise):
# data, mjd, error, second_data, aligned_data, aligned_second_data, aligned_mjd = white_noise()
a = FeatureSpace(featureList=['Con'] , Con=1)
a=a.calculateFeature(white_noise)
assert(a.result(method='array') >= 0.04 and a.result(method='array') <= 0.05)示例9: test_CAR
def test_CAR(fake_lc):
# data, mjd, error, second_data, aligned_data, aligned_second_data, aligned_mjd = fake_lc()
a = FeatureSpace(featureList=['CAR_sigma', 'CAR_tau', 'CAR_tmean'] , CAR_sigma=[mjd, error])
a=a.calculateFeature(fake_lc[0])
assert(a.result(method='array') >= 0.043 and a.result(method='array') <= 0.046)示例10: test_Beyond1Std
def test_Beyond1Std(white_noise):
# data, mjd, error, second_data, aligned_data, aligned_second_data, aligned_mjd = white_noise()
a = FeatureSpace(featureList=['Beyond1Std'])
a=a.calculateFeature(white_noise)
assert(a.result(method='array') >= 0.30 and a.result(method='array') <= 0.40)示例11: test_PercentDifferenceFluxPercentile
def test_PercentDifferenceFluxPercentile(fake_lc):
# data, mjd, error, second_data, aligned_data, aligned_second_data, aligned_mjd = fake_lc()
a = FeatureSpace(featureList=['PercentDifferenceFluxPercentile'])
a=a.calculateFeature(fake_lc[0])
assert(a.result(method='array') >= 0.043 and a.result(method='array') <= 0.046)示例12: test_Period_Psi
def test_Period_Psi(periodic_lc):
# data, mjd, error, second_data, aligned_data, aligned_second_data, aligned_mjd = white_noise()
a = FeatureSpace(featureList=['PeriodLS', 'Period_fit','Psi_CS','Psi_eta'])
a=a.calculateFeature(periodic_lc)
# print a.result(method='array'), len(periodic_lc[0])
assert(a.result(method='array')[0] >= 19 and a.result(method='array')[0] <= 21)示例13: test_FluxPercentile
def test_FluxPercentile(white_noise):
# data, mjd, error, second_data, aligned_data, aligned_second_data, aligned_mjd = white_noise()
a = FeatureSpace(featureList=['FluxPercentileRatioMid20','FluxPercentileRatioMid35','FluxPercentileRatioMid50','FluxPercentileRatioMid65','FluxPercentileRatioMid80'] )
a=a.calculateFeature(white_noise)
assert(a.result(method='array')[0] >= 0.145 and a.result(method='array')[0] <= 0.160)
assert(a.result(method='array')[1] >= 0.260 and a.result(method='array')[1] <= 0.290)
assert(a.result(method='array')[2] >= 0.350 and a.result(method='array')[2] <= 0.450)
assert(a.result(method='array')[3] >= 0.540 and a.result(method='array')[3] <= 0.580)
assert(a.result(method='array')[4] >= 0.760 and a.result(method='array')[4] <= 0.800)示例14: test_Stetson
def test_Stetson(white_noise):
# data, mjd, error, second_data, aligned_data, aligned_second_data, aligned_mjd = white_noise()
a = FeatureSpace(featureList=['SlottedA_length','StetsonK', 'StetsonK_AC', 'StetsonJ', 'StetsonL'])
a=a.calculateFeature(white_noise)
assert(a.result(method='array')[1] >= 0.790 and a.result(method='array')[1] <= 0.85)
assert(a.result(method='array')[2] >= 0.20 and a.result(method='array')[2] <= 0.45)
assert(a.result(method='array')[3] >= -0.1 and a.result(method='array')[3] <= 0.1)
assert(a.result(method='array')[4] >= -0.1 and a.result(method='array')[4] <= 0.1)示例15: calculate_features
def calculate_features(lc_fn, feature_list):
lc = ReadLC_MACHO(lc_fn)
[data, mjd, error] = lc.ReadLC()
preprocessed_data = Preprocess_LC(data, mjd, error)
fs = FeatureSpace(featureList=feature_list,
Automean=[0,0],
#Beyond1Std=[np.array(error)],
CAR_sigma=[mjd, error],
Eta_e=mjd,
LinearTrend=mjd,
MaxSlope=mjd,
PeriodLS=mjd,
Psi_CS=mjd
)
values = fs.calculateFeature(data)
value_dict = values.result(method='dict')
A, PH, scaledPH = calculate_periodic_features(mjd, data)
for i in range(len(A)):
for j in range(len(A[i])):
value_dict['freq'+str(i+1)+'_harmonics_amplitude_'+str(j)] = A[i][j]
value_dict['freq'+str(i+1)+'_harmonics_rel_phase_'+str(j)] = scaledPH[i][j]
return value_dict