def test_min_max_scaler_zero_variance_features():
# Check min max scaler on toy data with zero variance features
X = [[0., 1., +0.5], [0., 1., -0.1], [0., 1., +1.1]]
X_new = [[+0., 2., 0.5], [-1., 1., 0.0], [+0., 1., 1.5]]
# default params
scaler = MinMaxScaler()
X_trans = scaler.fit_transform(X)
X_expected_0_1 = [[0., 0., 0.5], [0., 0., 0.0], [0., 0., 1.0]]
assert_array_almost_equal(X_trans, X_expected_0_1)
X_trans_inv = scaler.inverse_transform(X_trans)
assert_array_almost_equal(X, X_trans_inv)
X_trans_new = scaler.transform(X_new)
X_expected_0_1_new = [[+0., 1., 0.500], [-1., 0., 0.083], [+0., 0., 1.333]]
assert_array_almost_equal(X_trans_new, X_expected_0_1_new, decimal=2)
# not default params
scaler = MinMaxScaler(feature_range=(1, 2))
X_trans = scaler.fit_transform(X)
X_expected_1_2 = [[1., 1., 1.5], [1., 1., 1.0], [1., 1., 2.0]]
assert_array_almost_equal(X_trans, X_expected_1_2)
# function interface
X_trans = minmax_scale(X)
assert_array_almost_equal(X_trans, X_expected_0_1)
X_trans = minmax_scale(X, feature_range=(1, 2))
assert_array_almost_equal(X_trans, X_expected_1_2)
def test_min_max_scaler_zero_variance_features():
# Check min max scaler on toy data with zero variance features
X = [[0., 1., +0.5],
[0., 1., -0.1],
[0., 1., +1.1]]
X_new = [[+0., 2., 0.5],
[-1., 1., 0.0],
[+0., 1., 1.5]]
# default params
scaler = MinMaxScaler()
X_trans = scaler.fit_transform(X)
X_expected_0_1 = [[0., 0., 0.5],
[0., 0., 0.0],
[0., 0., 1.0]]
assert_array_almost_equal(X_trans, X_expected_0_1)
X_trans_inv = scaler.inverse_transform(X_trans)
assert_array_almost_equal(X, X_trans_inv)
X_trans_new = scaler.transform(X_new)
X_expected_0_1_new = [[+0., 1., 0.500],
[-1., 0., 0.083],
[+0., 0., 1.333]]
assert_array_almost_equal(X_trans_new, X_expected_0_1_new, decimal=2)
# not default params
scaler = MinMaxScaler(feature_range=(1, 2))
X_trans = scaler.fit_transform(X)
X_expected_1_2 = [[1., 1., 1.5],
[1., 1., 1.0],
[1., 1., 2.0]]
assert_array_almost_equal(X_trans, X_expected_1_2)
# function interface
X_trans = minmax_scale(X)
assert_array_almost_equal(X_trans, X_expected_0_1)
X_trans = minmax_scale(X, feature_range=(1, 2))
assert_array_almost_equal(X_trans, X_expected_1_2)
def MyWavelets(data,MyWidths):
Widths = MyWidths
''' 将int型data转为float型sig '''
sig = np.ones(len(data),np.float) #产生空的float型sig
for i in range(0,len(data)):
sig[i] = float(data[i])
sig = np.array( sig )
wa = WaveletAnalysis(sig, wavelet=Morlet() )
# wavelet power spectrum
power = wa.wavelet_power
# scales
scales = wa.scales
# associated time vector
t = wa.time
# reconstruction of the original data
rx = wa.reconstruction()
########################################
# 数据逐帧 0-1标准化
# print(power.shape)
# power = np.transpose(power) #转置
power = power.T
# print(power.shape)
# # power_out = np.array([])
power_out = []
for i in power:
# # np.append(power_out, minmax_scale(i), axis = 0)
# power_out.append( minmax_scale(i).tolist() )
power_out.append( minmax_scale(i))
# print(max( minmax_scale(i) ))
# power_out = np.array(power_out)
return power_out
def test_minmax_scale_axis1():
X = iris.data
X_trans = minmax_scale(X, axis=1)
assert_array_almost_equal(np.min(X_trans, axis=1), 0)
assert_array_almost_equal(np.max(X_trans, axis=1), 1)
def test_minmax_scale_axis1():
X = iris.data
X_trans = minmax_scale(X, axis=1)
assert_array_almost_equal(np.min(X_trans, axis=1), 0)
assert_array_almost_equal(np.max(X_trans, axis=1), 1)
def minmax_demo():
data = [[2, 1, 4, 3], [1, 2, 3, 4], [4, 3, 2, 1]]
minmax = minmax_scale(data)
print(minmax)
from sklearn.preprocessing.data import minmax_scale
import numpy as np
X = np.array([[ 10., -1., 4.],
[ 2., 0., 5.],
[ 0., 0., -1.]])
# min_max_scaler = preprocessing.MinMaxScaler()
# X_minMax = min_max_scaler.minmax_scale(X)
for i in X:
X_minMax = minmax_scale(i)
print(X_minMax )