tra-analysis/analysis-master/tra_analysis/SVM.py
Arthur Lu 5d5d6c4c5e fixed/optimized imports,
fixed headers

Signed-off-by: Arthur Lu <learthurgo@gmail.com>
2021-11-09 22:52:04 +00:00

89 lines
2.5 KiB
Python

# Titan Robotics Team 2022: SVM submodule
# Written by Arthur Lu
# Notes:
# this should be imported as a python module using 'from tra_analysis import SVM'
# setup:
__version__ = "1.0.3"
__changelog__ = """changelog:
1.0.3:
- optimized imports
1.0.2:
- fixed __all__
1.0.1:
- removed unessasary self calls
- removed classness
1.0.0:
- ported analysis.SVM() here
"""
__author__ = (
"Arthur Lu <learthurgo@gmail.com>",
)
__all__ = [
"CustomKernel",
"StandardKernel",
"PrebuiltKernel",
"fit",
"eval_classification",
"eval_regression",
]
import sklearn
from . import ClassificationMetric, RegressionMetric
class CustomKernel:
def __new__(cls, C, kernel, degre, gamma, coef0, shrinking, probability, tol, cache_size, class_weight, verbose, max_iter, decision_function_shape, random_state):
return sklearn.svm.SVC(C = C, kernel = kernel, gamma = gamma, coef0 = coef0, shrinking = shrinking, probability = probability, tol = tol, cache_size = cache_size, class_weight = class_weight, verbose = verbose, max_iter = max_iter, decision_function_shape = decision_function_shape, random_state = random_state)
class StandardKernel:
def __new__(cls, kernel, C=1.0, degree=3, gamma='auto_deprecated', coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, decision_function_shape='ovr', random_state=None):
return sklearn.svm.SVC(C = C, kernel = kernel, gamma = gamma, coef0 = coef0, shrinking = shrinking, probability = probability, tol = tol, cache_size = cache_size, class_weight = class_weight, verbose = verbose, max_iter = max_iter, decision_function_shape = decision_function_shape, random_state = random_state)
class PrebuiltKernel:
class Linear:
def __new__(cls):
return sklearn.svm.SVC(kernel = 'linear')
class Polynomial:
def __new__(cls, power, r_bias):
return sklearn.svm.SVC(kernel = 'polynomial', degree = power, coef0 = r_bias)
class RBF:
def __new__(cls, gamma):
return sklearn.svm.SVC(kernel = 'rbf', gamma = gamma)
class Sigmoid:
def __new__(cls, r_bias):
return sklearn.svm.SVC(kernel = 'sigmoid', coef0 = r_bias)
def fit(kernel, train_data, train_outputs): # expects *2d data, 1d labels or outputs
return kernel.fit(train_data, train_outputs)
def eval_classification(kernel, test_data, test_outputs):
predictions = kernel.predict(test_data)
return ClassificationMetric(predictions, test_outputs)
def eval_regression(kernel, test_data, test_outputs):
predictions = kernel.predict(test_data)
return RegressionMetric(predictions, test_outputs)