alu/tra-analysis
1
0
Fork 0
mirror of https://github.com/titanscouting/tra-analysis.git synced 2024-12-26 01:29:10 +00:00
Code Issues Packages Projects Releases Wiki Activity

super ultra working

Browse Source
This commit is contained in:
Dev Singh 2020-03-06 12:43:01 -06:00
parent 46d1a48999
commit 12cbb21077
2 changed files with 9 additions and 18 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

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

Binary file not shown.

27
data analysis/analysis/analysis.py
View File

@ -348,17 +348,17 @@ def histo_analysis(hist_data):
def regression(inputs, outputs, args): # inputs, outputs expects N-D array def regression(inputs, outputs, args): # inputs, outputs expects N-D array
inputs = inputs[~np.isnan(inputs)] inputs = np.array(inputs)
outputs = outputs[~np.isnan(outputs)] outputs = np.array(outputs)
inputs = inputs[np.isfinite(inputs)]
outputs = outputs[np.isfinite(outputs)]
regressions = [] regressions = []
if 'lin' in args: # formula: ax + b if 'lin' in args: # formula: ax + b
try: try:
X = np.array(inputs)
y = np.array(outputs)
def func(x, a, b): def func(x, a, b):
return a * x + b return a * x + b
@ -375,9 +375,6 @@ def regression(inputs, outputs, args): # inputs, outputs expects N-D array
try: try:
X = np.array(inputs)
y = np.array(outputs)
def func(x, a, b, c, d): def func(x, a, b, c, d):
return a * np.log(b*(x + c)) + d return a * np.log(b*(x + c)) + d
@ -392,10 +389,7 @@ def regression(inputs, outputs, args): # inputs, outputs expects N-D array
if 'exp' in args: # formula: a e ^ (b(x + c)) + d if 'exp' in args: # formula: a e ^ (b(x + c)) + d
try: try:
X = np.array(inputs)
y = np.array(outputs)
def func(x, a, b, c, d): def func(x, a, b, c, d):
@ -411,8 +405,8 @@ def regression(inputs, outputs, args): # inputs, outputs expects N-D array
if 'ply' in args: # formula: a + bx^1 + cx^2 + dx^3 + ... if 'ply' in args: # formula: a + bx^1 + cx^2 + dx^3 + ...
inputs = [inputs] inputs = np.array([inputs])
outputs = [outputs] outputs = np.array([outputs])
plys = [] plys = []
limit = len(outputs[0]) limit = len(outputs[0])
@ -434,10 +428,7 @@ def regression(inputs, outputs, args): # inputs, outputs expects N-D array
if 'sig' in args: # formula: a tanh (b(x + c)) + d if 'sig' in args: # formula: a tanh (b(x + c)) + d
try: try:
X = np.array(inputs)
y = np.array(outputs)
def func(x, a, b, c, d): def func(x, a, b, c, d):