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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Classification configurations
TODO
add relevant ranges
set proper types for default configuration
find solution for string/float type in numpy
To change configurations, redefine values for respective keys in given dictionary. Any iterable with len() function is accepted, values returned should match type specified in defaults.
TOC
SVC
Generator
Defaults
Configs
Linear
Polynomial
Radial basis
Sigmoid
SVC<a id='svc'></a>
TODO
resolve class_weight and random_state parameters
Step1:  <a id='svc_poly'></a>
Step2:  <a id='svc_rbf'></a>
Step3:  <a id='svc_sigmoid'></a>
Step4:  SVC defaults<a id='svc_defaults'></a>
Step5:  SVC generator<a id='svc_gen'></a>
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Python Code:
def svc_linear_config():
    return {
        'C': (1.0,),
        'kernel': ('linear',),
        'shrinking': (True, False),
        'probability': (True, False),
        'tol': (0.001,),
        # 'class_weight': ('balanced',),
        'max_iter': (-1,),
        'decision_function_shape': ('ovo', 'ovr'),
    }
Explanation: Classification configurations
TODO
add relevant ranges
set proper types for default configuration
find solution for string/float type in numpy
To change configurations, redefine values for respective keys in given dictionary. Any iterable with len() function is accepted, values returned should match type specified in defaults.
TOC
SVC
Generator
Defaults
Configs
Linear
Polynomial
Radial basis
Sigmoid
SVC<a id='svc'></a>
TODO
resolve class_weight and random_state parameters
End of explanation
def svc_poly_config():
    return {
        'C': (1.0,),
        'kernel': ('poly',),
        'degree': (3,),
        'gamma': ('auto',),
        'coef0': (0.0,),
        'shrinking': (True, False),
        'probability': (True, False),
        'tol': (0.001,),
        # 'class_weight': ('balanced',),
        'max_iter': (-1,),
        'decision_function_shape': ('ovo', 'ovr'),
    }
Explanation: <a id='svc_poly'></a>
End of explanation
def svc_rbf_config():
    return {
        'C': (1.0,),
        'kernel': ('rbf',),
        'gamma': ('auto',),
        'shrinking': (True, False),
        'probability': (True, False),
        'tol': (0.001,),
        # 'class_weight': ('balanced',),
        'max_iter': (-1,),
        'decision_function_shape': ('ovo', 'ovr'),
    }
Explanation: <a id='svc_rbf'></a>
End of explanation
def svc_sigmoid_config():
    return {
        'C': (1.0,),
        'kernel': ('sigmoid',),
        'gamma': ('auto',),
        'coef0': (0.0,),
        'shrinking': (True, False),
        'probability': (True, False),
        'tol': (0.001,),
        # 'class_weight': ('balanced',),
        'max_iter': (-1,),
        'decision_function_shape': ('ovo', 'ovr'),
    }
Explanation: <a id='svc_sigmoid'></a>
End of explanation
svc_defaults = [('C', 'f4', 1.0),
                ('kernel', 'S10', 'rbf'),
                ('degree', 'i4', 3),
                ('gamma', 'S5', 'auto'),
                ('coef0', 'f4', 0.0),
                ('shrinking', 'b', True),
                ('probability', 'b', False),
                ('tol', 'f4', 0.001),
                ('cache_size', 'f4', 200),
                # ('class_weight', None),
                ('verbose', 'b', False),
                ('max_iter', 'i4', -1),
                ('decision_function_shape', 'S3', 'ovr'),
                # ('random_state', None)]
               ]
svc_configs = (svc_linear_config(),
               svc_poly_config(),
               svc_rbf_config(),
               svc_sigmoid_config())
Explanation: SVC defaults<a id='svc_defaults'></a>
End of explanation
import modules.utils as utils
import modules.values as values
utils.generate_parameters(values.parameters_path + 'svc', svc_configs, svc_defaults)
Explanation: SVC generator<a id='svc_gen'></a>
End of explanation
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