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analysis.py - v 1.0.7.000

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changelog:
        - added tanh_regression (logistical regression)
	- bug fixes
This commit is contained in:
ltcptgeneral 2018-11-28 16:35:47 -06:00
parent 4c87c425f8
commit 23f932ecf3
2 changed files with 46 additions and 12 deletions
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data analysis/analysis.py
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@ -7,10 +7,13 @@
#number of easter eggs: 2
#setup:
__version__ = "1.0.6.005"
__version__ = "1.0.7.000"
#changelog should be viewed using print(analysis.__changelog__)
__changelog__ = """changelog:
1.0.7.000:
- added tanh_regression (logistical regression)
- bug fixes
1.0.6.005:
- added z_normalize function to normalize dataset
- bug fixes
@ -119,6 +122,7 @@ import numbers
import numpy as np
import random
import scipy
from scipy.optimize import curve_fit
from sklearn import *
#import statistics <-- statistics.py functions have been integrated into analysis.py as of v 1.0.3.002
import time
@ -640,7 +644,7 @@ def log_regression(x, y, base):
_rms = rms(vals, y)
r2_d2 = r_squared(vals, y)
return [eq_str, _rms, r2_d2]
return eq_str, _rms, r2_d2
def exp_regression(x, y, base):
@ -660,7 +664,26 @@ def exp_regression(x, y, base):
_rms = rms(vals, y)
r2_d2 = r_squared(vals, y)
return [eq_str, _rms, r2_d2]
return eq_str, _rms, r2_d2
def tanh_regression(x, y):
def tanh (x, a, b, c, d):
return a * np.tanh(b * (x - c)) + d
reg_eq = np.float64(curve_fit(tanh, np.array(x), np.array(y))[0]).tolist()
eq_str = str(reg_eq[0]) + " * np.tanh(" + str(reg_eq[1]) + "*(z - " + str(reg_eq[2]) + ")) + " + str(reg_eq[3])
vals = []
for i in range(len(x)):
z = x[i]
exec("vals.append(" + eq_str + ")")
_rms = rms(vals, y)
r2_d2 = r_squared(vals, y)
return eq_str, _rms, r2_d2
def r_squared(predictions, targets): # assumes equal size inputs
@ -735,25 +758,36 @@ def optimize_regression(x, y, _range, resolution):#_range in poly regression is
r2s = []
for i in range (0, _range + 1, 1):
eqs.append(poly_regression(x_train, y_train, i)[0])
rmss.append(poly_regression(x_train, y_train, i)[1])
r2s.append(poly_regression(x_train, y_train, i)[2])
x, y, z = poly_regression(x_train, y_train, i)
eqs.append(x)
rmss.append(y)
r2s.append(z)
for i in range (1, 100 * resolution + 1):
try:
eqs.append(exp_regression(x_train, y_train, float(i / resolution))[0])
rmss.append(exp_regression(x_train, y_train, float(i / resolution))[1])
r2s.append(exp_regression(x_train, y_train, float(i / resolution))[2])
x, y, z = exp_regression(x_train, y_train, float(i / resolution))
eqs.append(x)
rmss.append(y)
r2s.append(z)
except:
pass
for i in range (1, 100 * resolution + 1):
try:
eqs.append(log_regression(x_train, y_train, float(i / resolution))[0])
rmss.append(log_regression(x_train, y_train, float(i / resolution))[1])
r2s.append(log_regression(x_train, y_train, float(i / resolution))[2])
x, y, z = log_regression(x_train, y_train, float(i / resolution))
eqs.append(x)
rmss.append(y)
r2s.append(z)
except:
pass
x, y, z = tanh_regression(x_train, y_train)
eqs.append(x)
rmss.append(y)
r2s.append(z)
print (eqs[::-1])
for i in range (0, len(eqs), 1): #marks all equations where r2 = 1 as they 95% of the time overfit the data
if r2s[i] == 1: