gregversteeg / gaussianize Goto Github PK

import gaussianize as g
import numpy as np

x_uni = np.random.uniform(size=(1000,1))
out_uni = g.Gaussianize()
out_uni.fit(x_uni)
print("out_uni coefficients:", out_uni.coefs_, "\n")
y_uni = out_uni.transform(x_uni)
print("sum of transform minus original uniform:", sum(y_uni-x_uni), "\n")

x_norm = np.random.normal(size=(1000,1))
out_norm = g.Gaussianize()
out_norm.fit(x_norm)
print("out_norm coefficients:", out_norm.coefs_, "\n")
y_norm = out_norm.transform(x_norm)
print("sum of transform minus original normal:", sum(y_norm-x_norm), "\n")

import scipy.io as scio
import os

midep1 = scio.loadmat(os.path.join("D:\\", "Shared", "Data - sLorTimeseries", "LorTimeSeries_2_EC_MID_001_ep1.mat"))["LoretaTimeSeries"]
x = midep1[0, :]
x.shape = (4001, 1)
out= g.Gaussianize()
out.fit(x)
print("my data coefficients:", out.coefs_, "\n")
y = out.transform(x)
print("tranform minus original of my data:")
print(y-x, "\n")
print("sum of transform minus original:", sum(y-x), `"\n")

out_uni coefficients: [(0.4964280888006252, 0.2880773918099591, 0.0)] 
sum of transform minus original uniform: [-8.04911693e-16] 
out_norm coefficients: [(-0.013567057474285795, 0.9626024748274751, 0.013488)] 
sum of transform minus original normal: [-0.15229739] 
my data coefficients: [(2.8030224, 1.3002187, 0.0)] 
tranform minus original of my data:
[[ 0.0000000e+00]
 [ 0.0000000e+00]
 [-1.3737008e-08]
 ...
 [ 0.0000000e+00]
 [ 0.0000000e+00]
 [ 0.0000000e+00]] 
sum of transform minus original: [1.0423828e-06] `