ml.py

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# This file was automatically generated by SWIG (https://www.swig.org).
# Version 4.1.0
#
# Do not make changes to this file unless you know what you are doing - modify
# the SWIG interface file instead.
 
from sys import version_info as _swig_python_version_info
# Import the low-level C/C++ module
if __package__ or "." in __name__:
    from . import _ml
else:
    import _ml
 
try:
    import builtins as __builtin__
except ImportError:
    import __builtin__
 
def _swig_repr(self):
    try:
        strthis = "proxy of " + self.this.__repr__()
    except __builtin__.Exception:
        strthis = ""
    return "<%s.%s; %s >" % (self.__class__.__module__, self.__class__.__name__, strthis,)
 
 
def _swig_setattr_nondynamic_instance_variable(set):
    def set_instance_attr(self, name, value):
        if name == "this":
            set(self, name, value)
        elif name == "thisown":
            self.this.own(value)
        elif hasattr(self, name) and isinstance(getattr(type(self), name), property):
            set(self, name, value)
        else:
            raise AttributeError("You cannot add instance attributes to %s" % self)
    return set_instance_attr
 
 
def _swig_setattr_nondynamic_class_variable(set):
    def set_class_attr(cls, name, value):
        if hasattr(cls, name) and not isinstance(getattr(cls, name), property):
            set(cls, name, value)
        else:
            raise AttributeError("You cannot add class attributes to %s" % cls)
    return set_class_attr
 
 
def _swig_add_metaclass(metaclass):
    """Class decorator for adding a metaclass to a SWIG wrapped class - a slimmed down version of six.add_metaclass"""
    def wrapper(cls):
        return metaclass(cls.__name__, cls.__bases__, cls.__dict__.copy())
    return wrapper
 
 
class _SwigNonDynamicMeta(type):
    """Meta class to enforce nondynamic attributes (no new attributes) for a class"""
    __setattr__ = _swig_setattr_nondynamic_class_variable(type.__setattr__)
 
 
class SwigPyIterator(object):
    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
 
    def __init__(self, *args, **kwargs):
        raise AttributeError("No constructor defined - class is abstract")
    __repr__ = _swig_repr
    __swig_destroy__ = _ml.delete_SwigPyIterator
 
    def value(self):
        return _ml.SwigPyIterator_value(self)
 
    def incr(self, n=1):
        return _ml.SwigPyIterator_incr(self, n)
 
    def decr(self, n=1):
        return _ml.SwigPyIterator_decr(self, n)
 
    def distance(self, x):
        return _ml.SwigPyIterator_distance(self, x)
 
    def equal(self, x):
        return _ml.SwigPyIterator_equal(self, x)
 
    def copy(self):
        return _ml.SwigPyIterator_copy(self)
 
    def next(self):
        return _ml.SwigPyIterator_next(self)
 
    def __next__(self):
        return _ml.SwigPyIterator___next__(self)
 
    def previous(self):
        return _ml.SwigPyIterator_previous(self)
 
    def advance(self, n):
        return _ml.SwigPyIterator_advance(self, n)
 
    def __eq__(self, x):
        return _ml.SwigPyIterator___eq__(self, x)
 
    def __ne__(self, x):
        return _ml.SwigPyIterator___ne__(self, x)
 
    def __iadd__(self, n):
        return _ml.SwigPyIterator___iadd__(self, n)
 
    def __isub__(self, n):
        return _ml.SwigPyIterator___isub__(self, n)
 
    def __add__(self, n):
        return _ml.SwigPyIterator___add__(self, n)
 
    def __sub__(self, *args):
        return _ml.SwigPyIterator___sub__(self, *args)
    def __iter__(self):
        return self
 
# Register SwigPyIterator in _ml:
_ml.SwigPyIterator_swigregister(SwigPyIterator)
class LinearRegression(object):
    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
    __repr__ = _swig_repr
    x = property(_ml.LinearRegression_x_get, _ml.LinearRegression_x_set)
    y = property(_ml.LinearRegression_y_get, _ml.LinearRegression_y_set)
    coeff = property(_ml.LinearRegression_coeff_get, _ml.LinearRegression_coeff_set)
    constant = property(_ml.LinearRegression_constant_get, _ml.LinearRegression_constant_set)
    sum_xy = property(_ml.LinearRegression_sum_xy_get, _ml.LinearRegression_sum_xy_set)
    sum_x = property(_ml.LinearRegression_sum_x_get, _ml.LinearRegression_sum_x_set)
    sum_y = property(_ml.LinearRegression_sum_y_get, _ml.LinearRegression_sum_y_set)
    sum_x_square = property(_ml.LinearRegression_sum_x_square_get, _ml.LinearRegression_sum_x_square_set)
    sum_y_square = property(_ml.LinearRegression_sum_y_square_get, _ml.LinearRegression_sum_y_square_set)
    x_train = property(_ml.LinearRegression_x_train_get, _ml.LinearRegression_x_train_set)
    y_train = property(_ml.LinearRegression_y_train_get, _ml.LinearRegression_y_train_set)
    x_test = property(_ml.LinearRegression_x_test_get, _ml.LinearRegression_x_test_set)
    y_test = property(_ml.LinearRegression_y_test_get, _ml.LinearRegression_y_test_set)
 
    def __init__(self):
        _ml.LinearRegression_swiginit(self, _ml.new_LinearRegression())
 
    def calculate_coeffecient(self):
        return _ml.LinearRegression_calculate_coeffecient(self)
 
    def calculate_constant(self):
        return _ml.LinearRegression_calculate_constant(self)
 
    def data_size(self):
        return _ml.LinearRegression_data_size(self)
 
    def return_coeffecient(self):
        return _ml.LinearRegression_return_coeffecient(self)
 
    def return_constant(self):
        return _ml.LinearRegression_return_constant(self)
 
    def best_fit(self):
        return _ml.LinearRegression_best_fit(self)
 
    def get_input(self, *args):
        return _ml.LinearRegression_get_input(self, *args)
 
    def split_data(self, test_size, seed, shuffle):
        return _ml.LinearRegression_split_data(self, test_size, seed, shuffle)
 
    def show_data(self):
        return _ml.LinearRegression_show_data(self)
 
    def predict(self, *args):
        return _ml.LinearRegression_predict(self, *args)
 
    def error_in(self, *args):
        return _ml.LinearRegression_error_in(self, *args)
 
    def error_square(self):
        return _ml.LinearRegression_error_square(self)
 
    def mse(self, x_data, y_data):
        return _ml.LinearRegression_mse(self, x_data, y_data)
 
    def r_sqrd(self, x_data, y_data):
        return _ml.LinearRegression_r_sqrd(self, x_data, y_data)
 
    def num_rows(self, input):
        return _ml.LinearRegression_num_rows(self, input)
    __swig_destroy__ = _ml.delete_LinearRegression
 
# Register LinearRegression in _ml:
_ml.LinearRegression_swigregister(LinearRegression)