def knn_smooth(data):
"""
func: 对欧式距离进行knn平滑(使用scikit-fda库进行平滑计算)
param: data: 欧式距离列表 - list
return: score: 平滑之后的得分
"""
smoothing_parameter = int(len(data) / 2)
fd = FDataGrid(sample_points=data,
data_matrix=[[i for i in range(1,
len(data) + 1)]])
knn = ks.KNeighborsSmoother(smoothing_parameter=smoothing_parameter)
fd_smoothed = knn.fit_transform(fd)
return [i[0] for i in fd_smoothed.data_matrix.round(2)[0]]
def smooth_datagrid(fd):
'''
Smoothes the values in the datagrid object
Args:
fd (datagrid): Includes data_matrix, x vector for contionous data
representation.
Returns:
knn_fd (datagrid): datagrid values smoothed with an kN Kernel.
'''
# smooth the values with kNeighbors kernel
param_values = np.linspace(start=2, stop=25, num=24)
knn = val.SmoothingParameterSearch(ks.KNeighborsSmoother(), param_values)
knn.fit(fd)
knn_fd = knn.transform(fd)
return knn_fd
param_values_others = param_values_knn / 32
# Local linear regression kernel smoothing.
llr = val.SmoothingParameterSearch(ks.LocalLinearRegressionSmoother(),
param_values_others)
llr.fit(fd)
llr_fd = llr.transform(fd)
# Nadaraya-Watson kernel smoothing.
nw = val.SmoothingParameterSearch(ks.NadarayaWatsonSmoother(),
param_values_others)
nw.fit(fd)
nw_fd = nw.transform(fd)
# K-nearest neighbours kernel smoothing.
knn = val.SmoothingParameterSearch(ks.KNeighborsSmoother(), param_values_knn)
knn.fit(fd)
knn_fd = knn.transform(fd)
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.plot(param_values_knn,
knn.cv_results_['mean_test_score'],
label='k-nearest neighbors')
ax.plot(param_values_knn,
llr.cv_results_['mean_test_score'],
label='local linear regression')
ax.plot(param_values_knn,
nw.cv_results_['mean_test_score'],
label='Nadaraya-Watson')
ax.legend()
dataset = skfda.datasets.fetch_phoneme()
fd = dataset['data'][:300]
fd[0:5].plot()
plt.show()
n_neighbors = np.arange(1, 24)
scale_factor = ((fd.domain_range[0][1] - fd.domain_range[0][0]) /
len(fd.grid_points[0]))
bandwidth = n_neighbors * scale_factor
# K-nearest neighbours kernel smoothing.
knn = val.SmoothingParameterSearch(
ks.KNeighborsSmoother(),
n_neighbors,
)
knn.fit(fd)
knn_fd = knn.transform(fd)
# Local linear regression kernel smoothing.
llr = val.SmoothingParameterSearch(
ks.LocalLinearRegressionSmoother(),
bandwidth,
)
llr.fit(fd)
llr_fd = llr.transform(fd)
# Nadaraya-Watson kernel smoothing.
nw = val.SmoothingParameterSearch(