alu/tra-analysis
1
0
Fork 0
mirror of https://github.com/titanscouting/tra-analysis.git synced 2024-12-27 01:59:08 +00:00
Code Issues Packages Projects Releases Wiki Activity

analysis.py - v 1.0.7.002

Browse Source 
changelog:
	- bug fixes
This commit is contained in:
ltcptgeneral 2018-11-29 12:58:53 -06:00
parent 9c67e6f927
commit 8a58fe28fa
2 changed files with 52 additions and 30 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

BIN
data analysis/__pycache__/analysis.cpython-37.pyc
View File

Binary file not shown.

82
data analysis/analysis.py
View File

@ -7,10 +7,12 @@
#number of easter eggs: 2
#setup:
__version__ = "1.0.7.001"
__version__ = "1.0.7.002"
#changelog should be viewed using print(analysis.__changelog__)
__changelog__ = """changelog:
1.0.7.002:
- bug fixes
1.0.7.001:
- bug fixes
1.0.7.000:
@ -622,7 +624,11 @@ def poly_regression(x, y, power):
for i in range(0, len(x), 1):
z = x[i]
exec("vals.append(" + eq_str + ")")
try:
exec("vals.append(" + eq_str + ")")
except:
pass
_rms = rms(vals, y)
r2_d2 = r_squared(vals, y)
@ -631,43 +637,57 @@ def poly_regression(x, y, power):
def log_regression(x, y, base):
x_fit = []
x_fit = []
for i in range(len(x)):
x_fit.append(np.log(x[i]) / np.log(base)) #change of base for logs
for i in range(len(x)):
try:
x_fit.append(np.log(x[i]) / np.log(base)) #change of base for logs
except:
pass
reg_eq = np.polyfit(x_fit, y, 1) # y = reg_eq[0] * log(x, base) + reg_eq[1]
eq_str = str(reg_eq[0]) + "* (np.log(z) / np.log(" + str(base) +"))+" + str(reg_eq[1])
vals = []
reg_eq = np.polyfit(x_fit, y, 1) # y = reg_eq[0] * log(x, base) + reg_eq[1]
q_str = str(reg_eq[0]) + "* (np.log(z) / np.log(" + str(base) +"))+" + str(reg_eq[1])
vals = []
for i in range(len(x)):
z = x[i]
exec("vals.append(" + eq_str + ")")
for i in range(len(x)):
z = x[i]
_rms = rms(vals, y)
r2_d2 = r_squared(vals, y)
try:
exec("vals.append(" + eq_str + ")")
except:
pass
return eq_str, _rms, r2_d2
_rms = rms(vals, y)
r2_d2 = r_squared(vals, y)
return eq_str, _rms, r2_d2
def exp_regression(x, y, base):
y_fit = []
y_fit = []
for i in range(len(y)):
y_fit.append(np.log(y[i]) / np.log(base)) #change of base for logs
for i in range(len(y)):
try:
y_fit.append(np.log(y[i]) / np.log(base)) #change of base for logs
except:
pass
reg_eq = np.polyfit(x, y_fit, 1, w=np.sqrt(y_fit)) # y = base ^ (reg_eq[0] * x) * base ^ (reg_eq[1])
eq_str = "(" + str(base) + "**(" + str(reg_eq[0]) + "*z))*(" + str(base) + "**(" + str(reg_eq[1]) + "))"
vals = []
reg_eq = np.polyfit(x, y_fit, 1, w=np.sqrt(y_fit)) # y = base ^ (reg_eq[0] * x) * base ^ (reg_eq[1])
eq_str = "(" + str(base) + "**(" + str(reg_eq[0]) + "*z))*(" + str(base) + "**(" + str(reg_eq[1]) + "))"
vals = []
for i in range(len(x)):
z = x[i]
exec("vals.append(" + eq_str + ")")
for i in range(len(x)):
z = x[i]
_rms = rms(vals, y)
r2_d2 = r_squared(vals, y)
try:
exec("vals.append(" + eq_str + ")")
except:
pass
return eq_str, _rms, r2_d2
_rms = rms(vals, y)
r2_d2 = r_squared(vals, y)
return eq_str, _rms, r2_d2
def tanh_regression(x, y):
@ -681,7 +701,11 @@ def tanh_regression(x, y):
for i in range(len(x)):
z = x[i]
exec("vals.append(" + eq_str + ")")
try:
exec("vals.append(" + eq_str + ")")
except:
pass
_rms = rms(vals, y)
r2_d2 = r_squared(vals, y)
@ -786,8 +810,6 @@ def optimize_regression(x, y, _range, resolution):#_range in poly regression is
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:
eqs[i] = ""
@ -914,7 +936,7 @@ def debug():
print(log_regression([1, 2, 3, 4], [2, 4, 8, 16], 2.717))
print(exp_regression([1, 2, 3, 4], [2, 4, 8, 16], 2.717))
x, y, z, o = optimize_regression([0, 1, 2, 3, 4], [1, 2, 4, 7, 19], 10, 100)
x, y, z, o = optimize_regression([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [1, 2, 4, 7, 19, 22, 30, 50, 60, 80], 10, 10)
for i in range(0, len(x), 1):
print(str(x[i]) + " | " + str(y[i]) + " | " + str(z[i]) + " | " + str(o[i][0]) + " | " + str(o[i][1]))