analysis.py v 1.1.9.000

data analysis/analysis/analysis.py

@@ -7,10 +7,13 @@
 #    current benchmark of optimization: 1.33 times faster
 # setup:
 
-__version__ = "1.1.8.000"
+__version__ = "1.1.9.000"
 
 # changelog should be viewed using print(analysis.__changelog__)
 __changelog__ = """changelog:
+    1.1.9.000:
+        - added SVM class, subclasses, and functions
+        - note: untested
     1.1.8.000:
         - added NaiveBayes classification engine
         - note: untested
@@ -471,6 +474,67 @@ class NaiveBayes:
 
         return model, cm, cr
 
+class SVM:
+
+    class CustomKernel:
+
+        def __init__(self, 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 __init__(self, 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 __init__(self):
+
+                return sklearn.svm.SVC(kernel = 'linear')
+
+        class Polynomial:
+
+            def __init__(self, power, r_bias):
+
+                return sklearn.svm.SVC(kernel = 'polynomial', degree = power, coef0 = r_bias)
+
+        class RBF:
+
+            def __init__(self, gamma):
+
+                return sklearn.svm.SVC(kernel = 'rbf', gamma = gamma)
+
+        class Sigmoid:
+
+            def __init__(self, r_bias):
+
+                return sklearn.svm.SVC(kernel = 'sigmoid', coef0 = r_bias)
+
+    def fit(self, kernel, train_data, train_outputs): # expects *2d data, 1d labels or outputs
+
+        return kernel.fit(train_data, train_outputs)
+
+    def eval_classification(self, kernel, test_data, test_outputs):
+
+        predictions = kernel.predict(test_data)
+        cm = sklearn.metrics.confusion_matrix(labels_test, predictions)
+        cr = sklearn.metrics.classification_report(labels_test, predictions)
+
+        return cm, cr
+
+    def eval_regression(self, kernel, test_data, test_outputs):
+
+        predictions = kernel.predict(test_data)
+        r_2 = r_squared(predictions, test_outputs)
+        mse = mse(predictions, test_outputs)
+        rms = rms(predictions, test_outputs)
+
+        return r_2, mse, rms
+
 class Regression:
 
     # Titan Robotics Team 2022: CUDA-based Regressions Module