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analysis.py - v 1.0.4.001

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changelog:
- added log regressions
- added exponential regressions
- added log_regression and exp_regression to __all__
This commit is contained in:
ltcptgeneral 2018-11-25 21:54:47 -06:00
parent cb0f5bb9c9
commit bfb820f157
2 changed files with 55 additions and 6 deletions
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analysis.py
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@ -8,7 +8,7 @@
#setup: #setup:
__version__ = "1.0.3.008" __version__ = "1.0.4.001"
__author__ = ( __author__ = (
"Arthur Lu <arthurlu@ttic.edu>, " "Arthur Lu <arthurlu@ttic.edu>, "
@ -27,6 +27,8 @@ __all__ = [
'stdev_z_split', 'stdev_z_split',
'histo_analysis', #histo_analysis_old is intentionally left out as it has been depreciated since v 1.0.1.005 'histo_analysis', #histo_analysis_old is intentionally left out as it has been depreciated since v 1.0.1.005
'poly_regression', 'poly_regression',
'log_regression',
'exp_regression',
'r_squared', 'r_squared',
'rms', 'rms',
'basic_analysis', 'basic_analysis',
@ -508,8 +510,6 @@ def poly_regression(x, y, power):
reg_eq = scipy.polyfit(x, y, deg = power) reg_eq = scipy.polyfit(x, y, deg = power)
print(reg_eq)
eq_str = "" eq_str = ""
for i in range(0, len(reg_eq), 1): for i in range(0, len(reg_eq), 1):
@ -522,19 +522,68 @@ def poly_regression(x, y, power):
vals = [] vals = []
for i in range(0, len(x), 1): for i in range(0, len(x), 1):
print(x[i])
z = x[i] z = x[i]
exec("vals.append(" + eq_str + ")") exec("vals.append(" + eq_str + ")")
print(vals)
_rms = rms(vals, y) _rms = rms(vals, y)
r2_d2 = r_squared(vals, y) r2_d2 = r_squared(vals, y)
return [eq_str, _rms, r2_d2] return [eq_str, _rms, r2_d2]
def log_regression(x, y, base):
x_fit = []
for i in range(len(x)):
x_fit.append(np.log(x[i]) / np.log(base)) #change of base for logs
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 = []
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 exp_regression(x, y, base):
y_fit = []
for i in range(len(y)):
y_fit.append(np.log(y[i]) / np.log(base))
reg_eq = np.polyfit(x, y_fit, 1, w=np.sqrt(y)) # 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 + ")")
_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 def r_squared(predictions, targets): # assumes equal size inputs
out = metrics.r2_score(targets, predictions) out = metrics.r2_score(targets, predictions)