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analysis.py v 1.1.11.000

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art 2019-11-08 13:20:38 -06:00
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commit 445ddf4d68
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data analysis/analysis/analysis.py
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@ -7,10 +7,13 @@
# current benchmark of optimization: 1.33 times faster
# setup:
__version__ = "1.1.10.000"
__version__ = "1.1.11.000"
# changelog should be viewed using print(analysis.__changelog__)
__changelog__ = """changelog:
1.1.11.000:
- added RandomForestClassifier and RandomForestRegressor
- note: untested
1.1.10.000:
- added numba.jit to remaining functions
1.1.9.002:
@ -551,6 +554,18 @@ class SVM:
return r_2, _mse, _rms
def RandomForestClassifier(data, labels, n_estimators="warn", criterion="gini", max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features="auto", max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, class_weight=None):
kernel = sklearn.ensemble.RandomForestClassifier(n_estimators = n_estimators, criterion = criterion, max_depth = max_depth, min_samples_split = min_samples_split, min_samples_leaf = min_samples_leaf, min_weight_fraction_leaf = min_weight_fraction_leaf, max_leaf_nodes = max_leaf_nodes, min_impurity_decrease = min_impurity_decrease, bootstrap = bootstrap, oob_score = oob_score, n_jobs = n_jobs, random_state = random_state, verbose = verbose, warm_start = warm_start, class_weight = class_weight)
kernel.fit(data, labels)
return kernel
def RandomForestRegressor(inputs, outputs, n_estimators="warn", criterion="mse", max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features="auto", max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False):
kernel = sklearn.ensemble.RandomForestRegressor(n_estimators = n_estimators, criterion = criterion, max_depth = max_depth, min_samples_split = min_samples_split, min_weight_fraction_leaf = min_weight_fraction_leaf, max_features = max_features, max_leaf_nodes = max_leaf_nodes, min_impurity_decrease = min_impurity_decrease, min_impurity_split = min_impurity_split, bootstrap = bootstrap, oob_score = oob_score, n_jobs = n_jobs, random_state = random_state, verbose = verbose, warm_start = warm_start)
kernel.fit(inputs, outputs)
return kernel
class Regression:
# Titan Robotics Team 2022: CUDA-based Regressions Module