def test_group_fpca_bases(basis, use_grid_points, exclude_groups):
n_basis = 9
n_components = 1
grid_points = np.arange(0.5, 365, 1)
fd_data = fetch_weather()["data"].coordinates[0]
fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), grid_points)
X = fd_data.data_matrix.squeeze()
X = np.hstack([X, X])
groups = [np.arange(0, 365), np.arange(365, 730)]
if use_grid_points:
grid_points = np.hstack([grid_points, grid_points])
else:
grid_points = None
gfpca = GroupFPCA(
n_components=n_components,
n_basis=n_basis,
basis_domain_range=(0, 365),
basis=basis,
groups=groups,
exclude_groups=exclude_groups,
).fit(X, grid_points=grid_points)
_ = gfpca.transform(X, grid_points=grid_points)
def test_groupfpca_errors():
n_basis = 9
n_components = 1
grid_points = np.arange(0.5, 365, 1)
fd_data = fetch_weather()["data"].coordinates[0]
fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), grid_points)
X = fd_data.data_matrix.squeeze()
X = np.hstack([X, X])
groups = [np.arange(0, 365), np.arange(365, 730)]
with pytest.raises(ValueError):
GroupFPCA(n_components=n_components,
n_basis=n_basis,
basis=object,
groups=groups).fit(X)
with pytest.raises(ValueError):
GroupFPCA(n_components=n_components,
n_basis=n_basis,
basis="error",
groups=groups).fit(X)
with pytest.raises(TypeError):
GroupFPCA(
n_components=n_components,
n_basis=n_basis,
groups=groups,
exclude_groups="error",
).fit(X)
def test_basis_fpca_transform_result(self):
n_basis = 9
n_components = 3
fd_data = fetch_weather()['data'].coordinates[0]
fd_data = FDataGrid(np.squeeze(fd_data.data_matrix),
np.arange(0.5, 365, 1))
# initialize basis data
basis = Fourier(n_basis=n_basis, domain_range=(0, 365))
fd_basis = fd_data.to_basis(basis)
fpca = FPCA(n_components=n_components,
regularization=TikhonovRegularization(
LinearDifferentialOperator(2),
regularization_parameter=1e5))
fpca.fit(fd_basis)
scores = fpca.transform(fd_basis)
# results obtained using Ramsay's R package
results = [[-7.68307641e+01, 5.69034443e+01, -1.22440149e+01],
[-9.02873996e+01, 1.46262257e+01, -1.78574536e+01],
[-8.21155683e+01, 3.19159491e+01, -2.56212328e+01],
[-1.14163637e+02, 3.66425562e+01, -1.00810836e+01],
[-6.97263223e+01, 1.22817168e+01, -2.39417618e+01],
[-6.41886364e+01, -1.07261045e+01, -1.10587407e+01],
[1.35824412e+02, 2.03484658e+01, -9.04815324e+00],
[-1.46816399e+01, -2.66867491e+01, -1.20233465e+01],
[1.02507511e+00, -2.29840736e+01, -9.06081296e+00],
[-3.62936903e+01, -2.09520442e+01, -1.14799951e+01],
[-4.20649313e+01, -1.13618094e+01, -6.24909009e+00],
[-7.38115985e+01, -3.18423866e+01, -1.50298626e+01],
[-6.69822456e+01, -3.35518632e+01, -1.25167352e+01],
[-1.03534763e+02, -1.29513941e+01, -1.49103879e+01],
[-1.04542036e+02, -1.36794907e+01, -1.41555965e+01],
[-7.35863347e+00, -1.41171956e+01, -2.97562788e+00],
[7.28804530e+00, -5.34421830e+01, -3.39823418e+00],
[5.59974094e+01, -4.02154080e+01, 3.78800103e-01],
[1.80778702e+02, 1.87798201e+01, -1.99043247e+01],
[-3.69700617e+00, -4.19441020e+01, 6.45820740e+00],
[3.76527216e+01, -4.23056953e+01, 1.04221757e+01],
[1.23850646e+02, -4.24648130e+01, -2.22336786e-01],
[-7.23588457e+00, -1.20579536e+01, 2.07502089e+01],
[-4.96871011e+01, 8.88483448e+00, 2.02882768e+01],
[-1.36726355e+02, -1.86472599e+01, 1.89076217e+01],
[-1.83878661e+02, 4.12118550e+01, 1.78960356e+01],
[-1.81568820e+02, 5.20817910e+01, 2.01078870e+01],
[-5.08775852e+01, 1.34600555e+01, 3.18602712e+01],
[-1.37633866e+02, 7.50809631e+01, 2.42320782e+01],
[4.98276375e+01, 1.33401270e+00, 3.50611066e+01],
[1.51149934e+02, -5.47417776e+01, 3.97592325e+01],
[1.58366096e+02, -3.80762686e+01, -5.62415023e+00],
[2.17139548e+02, 6.34055987e+01, -1.98853635e+01],
[2.33615480e+02, -7.90787574e-02, 2.69069525e+00],
[3.45371437e+02, 9.58703622e+01, 8.47570770e+00]]
results = np.array(results)
# compare results
np.testing.assert_allclose(scores, results, atol=1e-7)
def test_grid_fpca_transform_result(self):
n_components = 1
fd_data = fetch_weather()['data'].coordinates[0]
fpca = FPCA(n_components=n_components, weights=[1] * 365)
fpca.fit(fd_data)
scores = fpca.transform(fd_data)
# results obtained
results = [[-77.05020176], [-90.56072204], [-82.39565947],
[-114.45375934], [-69.99735931], [-64.44894047],
[135.58336775], [-14.93460852], [0.75024737], [-36.4781038],
[-42.35637749], [-73.98910492], [-67.11253749],
[-103.68269798], [-104.65948079],
[-7.42817782], [7.48125036], [56.29792942], [181.00258791],
[-3.53294736], [37.94673912], [124.43819913], [-7.04274676],
[-49.61134859], [-136.86256785], [-184.03502398],
[-181.72835749], [-51.06323208], [-137.85606731],
[50.10941466], [151.68118097], [159.01360046],
[217.17981302], [234.40195237], [345.39374006]]
results = np.array(results)
np.testing.assert_allclose(scores, results, rtol=1e-6)
def test_magnitude_shape_plot(self):
fd = fetch_weather()["data"]
fd_temperatures = FDataGrid(data_matrix=fd.data_matrix[:, :, 0],
sample_points=fd.sample_points,
dataset_label=fd.dataset_label,
axes_labels=fd.axes_labels[0:2])
msplot = MagnitudeShapePlot(fd_temperatures,
depth_method=modified_band_depth)
np.testing.assert_allclose(
msplot.points,
np.array([[0.2591055, 3.15861149], [1.3811996, 0.91806814],
[0.94648379, 2.75695426], [2.11346208, 7.24045853],
[0.82557436, 0.82727771], [1.23249759, 0.22004329],
[-2.66259589, 0.96925352], [0.15827963, 1.00235557],
[-0.43751765, 0.66236714], [0.70695162, 0.66482897],
[0.73095525, 0.33165117], [3.48445368, 12.59745018],
[3.15539264, 13.85234879], [3.52759979, 10.49810783],
[3.95518808, 15.28317686], [-0.48486514, 0.5035343],
[0.64492781, 6.83385521], [-0.83185751, 0.81125541],
[-3.47125418, 1.10683451], [0.22241054, 1.76783493],
[-0.54402406, 0.95229119], [-1.71310618, 0.61875513],
[-0.44161441, 0.77815135], [0.13851408, 1.02560672],
[7.59909246, 40.82126568], [7.57868277, 36.03923856],
[7.12930634, 45.96866318], [0.05746528, 1.75817588],
[1.53092075, 8.85227], [-1.48696387, 0.22472872],
[-2.853082, 4.50814844], [-2.42297615, 1.46926902],
[-5.8873129, 5.35742609], [-5.44346193, 5.18338576],
[-16.38949483, 0.94027717]]))
np.testing.assert_array_almost_equal(
msplot.outliers,
np.array([
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0,
0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1
]))
def test_magnitude_shape_plot(self):
fd = fetch_weather()["data"]
fd_temperatures = FDataGrid(data_matrix=fd.data_matrix[:, :, 0],
sample_points=fd.sample_points,
dataset_label=fd.dataset_label,
axes_labels=fd.axes_labels[0:2])
msplot = MagnitudeShapePlot(fd_temperatures, random_state=0)
np.testing.assert_allclose(
msplot.points,
np.array([[0.28216472, 3.15069249], [1.43406267, 0.77729052],
[0.96089808, 2.7302293], [2.1469911, 7.06601804],
[0.89081951, 0.71098079], [1.22591999, 0.2363983],
[-2.65530111, 0.9666511], [0.47819535, 0.83989187],
[-0.11256072, 0.89035836], [0.99627103, 0.3255725],
[0.77889317, 0.32451932], [3.47490723, 12.5630275],
[3.14828582, 13.80605804], [3.51793514, 10.46943904],
[3.94435195, 15.24142224], [0., 0.],
[0.74574282, 6.68207165], [-0.82501844, 0.82694929],
[-3.4617439, 1.10389229], [0.44523944, 1.61262494],
[-0.52255157, 1.00486028], [-1.67260144, 0.74626351],
[-0.10133788, 0.96326946], [0.36576472, 0.93071675],
[7.57827303, 40.70985885], [7.51140842, 36.65641988],
[7.13000185, 45.56574331], [0.28166597, 1.70861091],
[1.55486533, 8.75149947], [-1.43363018, 0.36935927],
[-2.79138743, 4.80007762], [-2.39987853, 1.54992208],
[-5.87118328, 5.34300766], [-5.42854833, 5.1694065],
[-16.34459211, 0.9397118]]))
np.testing.assert_array_almost_equal(
msplot.outliers,
np.array([
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0,
0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1
]))
def test_magnitude_shape_plot(self):
fd = fetch_weather()["data"]
fd_temperatures = fd.coordinates[0]
msplot = MagnitudeShapePlot(
fd_temperatures, multivariate_depth=SimplicialDepth())
np.testing.assert_allclose(msplot.points,
np.array([[0.2112587, 3.0322570],
[1.2823448, 0.8272850],
[0.8646544, 1.8619370],
[1.9862512, 5.5287354],
[0.7534918, 0.7203502],
[1.1325291, 0.2808455],
[-2.650529, 0.9702889],
[0.1434387, 0.9159834],
[-0.402844, 0.6413531],
[0.6354411, 0.6934311],
[0.5727553, 0.4628254],
[3.0524899, 8.8008899],
[2.7355803, 10.338497],
[3.1179374, 7.0686220],
[3.4944047, 11.479432],
[-0.402532, 0.5253690],
[0.5782190, 5.5400704],
[-0.839887, 0.7350041],
[-3.456470, 1.1156415],
[0.2260207, 1.5071672],
[-0.561562, 0.8836978],
[-1.690263, 0.6392155],
[-0.385394, 0.7401909],
[0.1467050, 0.9090058],
[7.1811993, 39.003407],
[6.8943132, 30.968126],
[6.6227164, 41.448548],
[0.0726709, 1.5960063],
[1.4450617, 8.7183435],
[-1.459836, 0.2719813],
[-2.824349, 4.5729382],
[-2.390462, 1.5464775],
[-5.869571, 5.3517279],
[-5.426019, 5.1817219],
[-16.34459, 0.9397117]]), rtol=1e-5)
np.testing.assert_array_almost_equal(msplot.outliers,
np.array(
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 1, 1, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 1, 1, 0, 1, 0,
0, 0, 0, 0, 1]))
def test_basis_fpca_fit_result(self):
n_basis = 9
n_components = 3
fd_data = fetch_weather()['data'].coordinates[0]
fd_data = FDataGrid(np.squeeze(fd_data.data_matrix),
np.arange(0.5, 365, 1))
# initialize basis data
basis = Fourier(n_basis=n_basis, domain_range=(0, 365))
fd_basis = fd_data.to_basis(basis)
fpca = FPCA(n_components=n_components,
regularization=TikhonovRegularization(
LinearDifferentialOperator(2),
regularization_parameter=1e5))
fpca.fit(fd_basis)
# results obtained using Ramsay's R package
results = [[
0.92407552, 0.13544888, 0.35399023, 0.00805966, -0.02148108,
-0.01709549, -0.00208469, -0.00297439, -0.00308224
],
[
-0.33314436, -0.05116842, 0.89443418, 0.14673902,
0.21559073, 0.02046924, 0.02203431, -0.00787185,
0.00247492
],
[
-0.14241092, 0.92131899, 0.00514715, 0.23391411,
-0.19497613, 0.09800817, 0.01754439, -0.00205874,
0.01438185
]]
results = np.array(results)
# compare results obtained using this library. There are slight
# variations due to the fact that we are in two different packages
for i in range(n_components):
if np.sign(fpca.components_.coefficients[i][0]) != np.sign(
results[i][0]):
results[i, :] *= -1
np.testing.assert_allclose(fpca.components_.coefficients,
results,
atol=1e-7)
def test_basis_fpca_regularization_fit_result(self):
n_basis = 9
n_components = 3
fd_data = fetch_weather()['data'].coordinates[0]
fd_data = FDataGrid(np.squeeze(fd_data.data_matrix),
np.arange(0.5, 365, 1))
# initialize basis data
basis = Fourier(n_basis=n_basis, domain_range=(0, 365))
fd_basis = fd_data.to_basis(basis)
fpca = FPCA(n_components=n_components)
fpca.fit(fd_basis)
# results obtained using Ramsay's R package
results = [[
0.9231551, 0.1364966, 0.3569451, 0.0092012, -0.0244525,
-0.02923873, -0.003566887, -0.009654571, -0.0100063
],
[
-0.3315211, -0.0508643, 0.89218521, 0.1669182,
0.2453900, 0.03548997, 0.037938051, -0.025777507,
0.008416904
],
[
-0.1379108, 0.9125089, 0.00142045, 0.2657423,
-0.2146497, 0.16833314, 0.031509179, -0.006768189,
0.047306718
]]
results = np.array(results)
# compare results obtained using this library. There are slight
# variations due to the fact that we are in two different packages
for i in range(n_components):
if np.sign(fpca.components_.coefficients[i][0]) != np.sign(
results[i][0]):
results[i, :] *= -1
np.testing.assert_allclose(fpca.components_.coefficients,
results,
atol=1e-7)
def test_group_fpca_matches_skfda_results():
n_basis = 9
n_components = 3
grid_points = np.arange(0.5, 365, 1)
fd_data = fetch_weather()["data"].coordinates[0]
fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), grid_points)
X = fd_data.data_matrix.squeeze()
X = np.hstack([X, X])
groups = [np.arange(0, 365), np.arange(365, 730)]
gfpca = GroupFPCA(
n_components=n_components,
n_basis=n_basis,
basis_domain_range=(0, 365),
basis="Fourier",
groups=groups,
)
_ = gfpca.fit_transform(X,
grid_points=np.hstack([grid_points, grid_points]))
results = [
[
0.9231551,
0.1364966,
0.3569451,
0.0092012,
-0.0244525,
-0.02923873,
-0.003566887,
-0.009654571,
-0.0100063,
],
[
-0.3315211,
-0.0508643,
0.89218521,
0.1669182,
0.2453900,
0.03548997,
0.037938051,
-0.025777507,
0.008416904,
],
[
-0.1379108,
0.9125089,
0.00142045,
0.2657423,
-0.2146497,
0.16833314,
0.031509179,
-0.006768189,
0.047306718,
],
]
results = np.array(results)
for grp_idx in range(len(groups)):
# The sign of the components is arbitrary so we change the sign if necessary
for i in range(n_components):
if np.sign(
gfpca.components_[grp_idx].coefficients[i][0]) != np.sign(
results[i][0]):
results[i, :] *= -1
np.testing.assert_allclose(gfpca.components_[grp_idx].coefficients,
results,
atol=1e-7)
# License: MIT
# sphinx_gallery_thumbnail_number = 2
import matplotlib.pyplot as plt
import numpy as np
from skfda import datasets
from skfda.exploratory.depth import fraiman_muniz_depth, modified_band_depth
from skfda.exploratory.visualization import MagnitudeShapePlot
##############################################################################
# First, the Canadian Weather dataset is downloaded from the package 'fda' in
# CRAN. It contains a FDataGrid with daily temperatures and precipitations,
# that is, it has a 2-dimensional image. We are interested only in the daily
# average temperatures, so we extract the first coordinate.
dataset = datasets.fetch_weather()
fd = dataset["data"]
fd_temperatures = fd.coordinates[0]
##############################################################################
# The data is plotted to show the curves we are working with. They are divided
# according to the target. In this case, it includes the different climates to
# which the weather stations belong.
# Each climate is assigned a color. Defaults to grey.
colormap = plt.cm.get_cmap('seismic')
label_names = dataset["target_names"]
nlabels = len(label_names)
label_colors = colormap(np.arange(nlabels) / (nlabels - 1))
fd_temperatures.plot(group=dataset["target"],
from skfda import datasets
from skfda.exploratory.visualization.clustering import (plot_clusters,
plot_cluster_lines,
plot_cluster_bars)
from skfda.ml.clustering import KMeans, FuzzyCMeans
import matplotlib.pyplot as plt
import numpy as np
##############################################################################
# First, the Canadian Weather dataset is downloaded from the package 'fda' in
# CRAN. It contains a FDataGrid with daily temperatures and precipitations,
# that is, it has a 2-dimensional image. We are interested only in the daily
# average temperatures, so we select the first coordinate function.
X, y = datasets.fetch_weather(return_X_y=True, as_frame=True)
fd = X.iloc[:, 0].values
fd_temperatures = fd.coordinates[0]
target = y.values
# The desired FDataGrid only contains 10 random samples, so that the example
# provides clearer plots.
indices_samples = np.array([1, 3, 5, 10, 14, 17, 21, 25, 27, 30])
fd = fd_temperatures[indices_samples]
##############################################################################
# The data is plotted to show the curves we are working with. They are divided
# according to the target. In this case, it includes the different climates to
# which the weather stations belong to.
climates = target[indices_samples].remove_unused_categories()
def test_grid_fpca_regularization_fit_result(self):
n_components = 1
fd_data = fetch_weather()['data'].coordinates[0]
fd_data = FDataGrid(np.squeeze(fd_data.data_matrix),
np.arange(0.5, 365, 1))
fpca = FPCA(n_components=n_components,
weights=[1] * 365,
regularization=TikhonovRegularization(
LinearDifferentialOperator(2)))
fpca.fit(fd_data)
# results obtained using fda.usc for the first component
results = [[
-0.06961236, -0.07027042, -0.07090496, -0.07138247, -0.07162215,
-0.07202264, -0.07264893, -0.07279174, -0.07274672, -0.07300075,
-0.07365471, -0.07489002, -0.07617455, -0.07658708, -0.07551923,
-0.07375128, -0.0723776, -0.07138373, -0.07080555, -0.07111745,
-0.0721514, -0.07395427, -0.07558341, -0.07650959, -0.0766541,
-0.07641352, -0.07660864, -0.07669081, -0.0765396, -0.07640671,
-0.07634668, -0.07626304, -0.07603638, -0.07549114, -0.07410347,
-0.07181791, -0.06955356, -0.06824034, -0.06834077, -0.06944125,
-0.07133598, -0.07341109, -0.07471501, -0.07568844, -0.07631904,
-0.07647264, -0.07629453, -0.07598431, -0.07628157, -0.07654062,
-0.07616026, -0.07527189, -0.07426683, -0.07267961, -0.07079998,
-0.06927394, -0.068412, -0.06838534, -0.06888439, -0.0695309,
-0.07005508, -0.07066637, -0.07167196, -0.07266978, -0.07275299,
-0.07235183, -0.07207819, -0.07159814, -0.07077697, -0.06977026,
-0.0691952, -0.06965756, -0.07058327, -0.07075751, -0.07025415,
-0.06954233, -0.06899785, -0.06891026, -0.06887079, -0.06862183,
-0.06830082, -0.06777765, -0.06700202, -0.06639394, -0.06582435,
-0.06514987, -0.06467236, -0.06425272, -0.06359187, -0.062922,
-0.06300068, -0.06325494, -0.06316979, -0.06296254, -0.06246343,
-0.06136836, -0.0600936, -0.05910688, -0.05840872, -0.0576547,
-0.05655684, -0.05546518, -0.05484433, -0.05465746, -0.05449286,
-0.05397004, -0.05300742, -0.05196686, -0.05133129, -0.05064617,
-0.04973418, -0.04855687, -0.04714356, -0.04588103, -0.04547284,
-0.04571493, -0.04580704, -0.04523509, -0.04457293, -0.04405309,
-0.04338468, -0.04243512, -0.04137278, -0.04047946, -0.03984531,
-0.03931376, -0.0388847, -0.03888507, -0.03908662, -0.03877577,
-0.03830952, -0.03802713, -0.03773521, -0.03752388, -0.03743759,
-0.03714113, -0.03668387, -0.0363703, -0.03642288, -0.03633051,
-0.03574618, -0.03486536, -0.03357797, -0.03209969, -0.0306837,
-0.02963987, -0.029102, -0.0291513, -0.02932013, -0.02912619,
-0.02869407, -0.02801974, -0.02732363, -0.02690451, -0.02676622,
-0.0267323, -0.02664896, -0.02661708, -0.02637166, -0.02577496,
-0.02490428, -0.02410813, -0.02340367, -0.02283356, -0.02246305,
-0.0224229, -0.0225435, -0.02295603, -0.02324663, -0.02310005,
-0.02266893, -0.02221522, -0.02168056, -0.02129419, -0.02064909,
-0.02007801, -0.01979083, -0.01979541, -0.01978879, -0.01954269,
-0.0191623, -0.01879572, -0.01849678, -0.01810297, -0.01769666,
-0.01753802, -0.01794351, -0.01871307, -0.01930005, -0.01933,
-0.01901017, -0.01873486, -0.01861838, -0.01870777, -0.01879,
-0.01904219, -0.01945078, -0.0200607, -0.02076936, -0.02100213,
-0.02071439, -0.02052113, -0.02076313, -0.02128468, -0.02175631,
-0.02206387, -0.02201054, -0.02172142, -0.02143092, -0.02133647,
-0.02144956, -0.02176286, -0.02212579, -0.02243861, -0.02278316,
-0.02304113, -0.02313356, -0.02349275, -0.02417028, -0.0245954,
-0.0244062, -0.02388557, -0.02374682, -0.02401071, -0.02431126,
-0.02433125, -0.02427656, -0.02430442, -0.02424977, -0.02401619,
-0.02402294, -0.02415424, -0.02413262, -0.02404076, -0.02397651,
-0.0243893, -0.0253322, -0.02664395, -0.0278802, -0.02877936,
-0.02927182, -0.02937318, -0.02926277, -0.02931632, -0.02957945,
-0.02982133, -0.03023224, -0.03060406, -0.03066011, -0.03070932,
-0.03116429, -0.03179009, -0.03198094, -0.03149462, -0.03082037,
-0.03041594, -0.0303307, -0.03028465, -0.03052841, -0.0311837,
-0.03199307, -0.03262025, -0.03345083, -0.03442665, -0.03521313,
-0.0356433, -0.03606037, -0.03677406, -0.03735165, -0.03746578,
-0.03744154, -0.03752143, -0.03780898, -0.03837639, -0.03903232,
-0.03911629, -0.03857567, -0.03816592, -0.03819285, -0.03818405,
-0.03801684, -0.03788493, -0.03823232, -0.03906142, -0.04023251,
-0.04112434, -0.04188011, -0.04254759, -0.043, -0.04340181,
-0.04412687, -0.04484482, -0.04577669, -0.04700832, -0.04781373,
-0.04842662, -0.04923723, -0.05007637, -0.05037817, -0.05009794,
-0.04994083, -0.05012712, -0.05094001, -0.05216065, -0.05350458,
-0.05469781, -0.05566309, -0.05641011, -0.05688106, -0.05730818,
-0.05759156, -0.05763771, -0.05760073, -0.05766117, -0.05794587,
-0.05816696, -0.0584046, -0.05905105, -0.06014331, -0.06142231,
-0.06270788, -0.06388225, -0.06426245, -0.06386721, -0.0634656,
-0.06358049, -0.06442514, -0.06570047, -0.06694328, -0.0682621,
-0.06897846, -0.06896583, -0.06854621, -0.06797142, -0.06763755,
-0.06784024, -0.06844314, -0.06918567, -0.07021928, -0.07148473,
-0.07232504, -0.07272276, -0.07287021, -0.07289836, -0.07271531,
-0.07239956, -0.07214086, -0.07170078, -0.07081195, -0.06955202,
-0.06825156, -0.06690167, -0.06617102, -0.06683291, -0.06887539,
-0.07089424, -0.07174837, -0.07150888, -0.07070378, -0.06960066,
-0.06842496, -0.06777666, -0.06728403, -0.06681262, -0.06679066
]]
results = np.array(results)
# compare results obtained using this library. There are slight
# variations due to the fact that we are in two different packages
for i in range(n_components):
if np.sign(fpca.components_.data_matrix[i][0]) != np.sign(
results[i][0]):
results[i, :] *= -1
np.testing.assert_allclose(fpca.components_.data_matrix.reshape(
fpca.components_.data_matrix.shape[:-1]),
results,
rtol=1e-2)
def test_grid_fpca_fit_result(self):
n_components = 1
fd_data = fetch_weather()['data'].coordinates[0]
fpca = FPCA(n_components=n_components, weights=[1] * 365)
fpca.fit(fd_data)
# results obtained using fda.usc for the first component
results = [[
-0.06958281, -0.07015412, -0.07095115, -0.07185632, -0.07128256,
-0.07124209, -0.07364828, -0.07297663, -0.07235438, -0.07307498,
-0.07293423, -0.07449293, -0.07647909, -0.07796823, -0.07582476,
-0.07263243, -0.07241871, -0.0718136, -0.07015477, -0.07132331,
-0.0711527, -0.07435933, -0.07602666, -0.0769783, -0.07707199,
-0.07503802, -0.0770302, -0.07705581, -0.07633515, -0.07624817,
-0.07631568, -0.07619913, -0.07568, -0.07595155, -0.07506939,
-0.07181941, -0.06907624, -0.06735476, -0.06853985, -0.06902363,
-0.07098882, -0.07479412, -0.07425241, -0.07555835, -0.0765903,
-0.07651853, -0.07682536, -0.07458996, -0.07631711, -0.07726509,
-0.07641246, -0.0744066, -0.07501397, -0.07302722, -0.07045571,
-0.06912529, -0.06792186, -0.06830739, -0.06898433, -0.07000192,
-0.07014513, -0.06994886, -0.07115909, -0.073999, -0.07292669,
-0.07139879, -0.07226865, -0.07187915, -0.07122995, -0.06975022,
-0.06800613, -0.06900793, -0.07186378, -0.07114479, -0.07015252,
-0.06944782, -0.068291, -0.06905348, -0.06925773, -0.06834624,
-0.06837319, -0.06824067, -0.06644614, -0.06637313, -0.06626312,
-0.06470209, -0.0645058, -0.06477729, -0.06411049, -0.06158499,
-0.06305197, -0.06398006, -0.06277579, -0.06282124, -0.06317684,
-0.0614125, -0.05961922, -0.05875443, -0.05845781, -0.05828608,
-0.05666474, -0.05495706, -0.05446301, -0.05468254, -0.05478609,
-0.05440798, -0.05312339, -0.05102368, -0.05160285, -0.05077954,
-0.04979648, -0.04890853, -0.04745462, -0.04496763, -0.0448713,
-0.04599596, -0.04688998, -0.04488872, -0.04404507, -0.04420729,
-0.04368153, -0.04254381, -0.0411764, -0.04022811, -0.03999746,
-0.03963634, -0.03832502, -0.0383956, -0.04015374, -0.0387544,
-0.03777315, -0.03830728, -0.03768616, -0.03714081, -0.03781918,
-0.03739374, -0.03659894, -0.03563342, -0.03658407, -0.03686991,
-0.03543746, -0.03518799, -0.03361226, -0.0321534, -0.03050438,
-0.02958411, -0.02855023, -0.02913402, -0.02992464, -0.02899548,
-0.02891629, -0.02809554, -0.02702642, -0.02672194, -0.02678648,
-0.02698471, -0.02628085, -0.02674285, -0.02658515, -0.02604447,
-0.0245711, -0.02413174, -0.02342496, -0.022898, -0.02216152,
-0.02272283, -0.02199741, -0.02305362, -0.02371371, -0.02320865,
-0.02234777, -0.0225018, -0.02104359, -0.02203346, -0.02052545,
-0.01987457, -0.01947911, -0.01986949, -0.02012196, -0.01958515,
-0.01906753, -0.01857869, -0.01874101, -0.01827973, -0.017752,
-0.01702056, -0.01759611, -0.01888485, -0.01988159, -0.01951675,
-0.01872967, -0.01866667, -0.0183576, -0.01909758, -0.018599,
-0.01910036, -0.01930315, -0.01958856, -0.02129936, -0.0216614,
-0.0204397, -0.02002368, -0.02058828, -0.02149915, -0.02167326,
-0.02238569, -0.02211907, -0.02168336, -0.02124387, -0.02131655,
-0.02130508, -0.02181227, -0.02230632, -0.02223732, -0.0228216,
-0.02355137, -0.02275145, -0.02286893, -0.02437776, -0.02523897,
-0.0248354, -0.02319174, -0.02335831, -0.02405789, -0.02483273,
-0.02428119, -0.02395295, -0.02437185, -0.02476434, -0.02347973,
-0.02385957, -0.02451257, -0.02414586, -0.02439035, -0.02357782,
-0.02417295, -0.02504764, -0.02682569, -0.02807111, -0.02886335,
-0.02943406, -0.02956806, -0.02893096, -0.02903812, -0.02999862,
-0.029421, -0.03016203, -0.03118823, -0.03076205, -0.03005985,
-0.03079187, -0.03215188, -0.03271075, -0.03146124, -0.03040965,
-0.03008436, -0.03085897, -0.03015341, -0.03014661, -0.03110255,
-0.03271278, -0.03217399, -0.0331721, -0.03459221, -0.03572073,
-0.03560707, -0.03531492, -0.03687657, -0.03800143, -0.0373808,
-0.03729927, -0.03748666, -0.03754171, -0.03790408, -0.03963726,
-0.03992153, -0.03812243, -0.0373844, -0.0385394, -0.03849716,
-0.03826345, -0.03743958, -0.0380861, -0.03857622, -0.04099357,
-0.04102509, -0.04170207, -0.04283573, -0.04320618, -0.04269438,
-0.04467527, -0.04470603, -0.04496092, -0.04796417, -0.04796633,
-0.047863, -0.04883668, -0.0505939, -0.05112441, -0.04960962,
-0.05000041, -0.04962112, -0.05087008, -0.0521671, -0.05369792,
-0.05478139, -0.05559221, -0.05669698, -0.05654505, -0.05731113,
-0.05783543, -0.05766056, -0.05754354, -0.05724272, -0.05831026,
-0.05847512, -0.05804533, -0.05875046, -0.06021703, -0.06147975,
-0.06213918, -0.0645805, -0.06500849, -0.06361716, -0.06315227,
-0.06306436, -0.06425743, -0.06626847, -0.06615213, -0.06881004,
-0.06942296, -0.06889225, -0.06868663, -0.0678667, -0.06720133,
-0.06771172, -0.06885042, -0.06896979, -0.06961627, -0.07211988,
-0.07252956, -0.07265559, -0.07264195, -0.07306334, -0.07282035,
-0.07196505, -0.07210595, -0.07203942, -0.07105821, -0.06920599,
-0.06892264, -0.06699939, -0.06537829, -0.06543323, -0.06913186,
-0.07210039, -0.07219987, -0.07124228, -0.07065497, -0.06996833,
-0.0674457, -0.06800847, -0.06784175, -0.06592871, -0.06723401
]]
results = np.array(results)
# compare results obtained using this library. There are slight
# variations due to the fact that we are in two different packages
for i in range(n_components):
if np.sign(fpca.components_.data_matrix[i][0]) != np.sign(
results[i][0]):
results[i, :] *= -1
np.testing.assert_allclose(fpca.components_.data_matrix.reshape(
fpca.components_.data_matrix.shape[:-1]),
results,
rtol=1e-6)
