def _num_internal(lat1, lng1, lat2, lng2, method, *args, **kwargs): """ Internal function to compute the numeric similarity algorithms. """ # compute the 1D distance between the values d = _haversine_distance(lat1, lng1, lat2, lng2) if method == 'step': num_sim_alg = _step_sim elif method in ['linear', 'lin']: num_sim_alg = _linear_sim elif method == 'squared': num_sim_alg = _squared_sim elif method in ['exp', 'exponential']: num_sim_alg = _exp_sim elif method in ['gauss', 'gaussian']: num_sim_alg = _gauss_sim else: raise ValueError( "The algorithm '{}' is not known.".format(method) ) return num_sim_alg(d, *args, **kwargs)
def _compute_vectorized(self, lat1, lng1, lat2, lng2): d = _haversine_distance(lat1, lng1, lat2, lng2) if self.method == 'step': num_sim_alg = partial(_step_sim, d, self.offset, self.origin) elif self.method in ['linear', 'lin']: num_sim_alg = partial(_linear_sim, d, self.scale, self.offset, self.origin) elif self.method == 'squared': num_sim_alg = partial(_squared_sim, d, self.scale, self.offset, self.origin) elif self.method in ['exp', 'exponential']: num_sim_alg = partial(_exp_sim, d, self.scale, self.offset, self.origin) elif self.method in ['gauss', 'gaussian']: num_sim_alg = partial(_gauss_sim, d, self.scale, self.offset, self.origin) else: raise ValueError("The algorithm '{}' is not known.".format( self.method)) c = num_sim_alg() c = _fillna(c, self.missing_value) return c
def _num_internal(lat1, lng1, lat2, lng2, method, *args, **kwargs): """ Internal function to compute the numeric similarity algorithms. """ # compute the 1D distance between the values d = _haversine_distance(lat1, lng1, lat2, lng2) if method == 'step': num_sim_alg = _step_sim elif method in ['linear', 'lin']: num_sim_alg = _linear_sim elif method == 'squared': num_sim_alg = _squared_sim elif method in ['exp', 'exponential']: num_sim_alg = _exp_sim elif method in ['gauss', 'gaussian']: num_sim_alg = _gauss_sim else: raise ValueError( "The algorithm '{}' is not known.".format(method) ) return num_sim_alg(d, *args, **kwargs)
def _num_internal(lat1, lng1, lat2, lng2, call_method, *args, **kwargs): """Internal function to compute the numeric similarity algorithms.""" # compute the 1D distance between the values d = _haversine_distance(lat1, lng1, lat2, lng2) return call_method(d, *args, **kwargs)
def _compute_vectorized(self, lat1, lng1, lat2, lng2): if self.method == 'step': num_sim_alg = _step_sim elif self.method in ['linear', 'lin']: num_sim_alg = _linear_sim elif self.method == 'squared': num_sim_alg = _squared_sim elif self.method in ['exp', 'exponential']: num_sim_alg = _exp_sim elif self.method in ['gauss', 'gaussian']: num_sim_alg = _gauss_sim else: raise ValueError( "The algorithm '{}' is not known.".format(self.method)) d = _haversine_distance(lat1, lng1, lat2, lng2) c = num_sim_alg(d, *self.args, **self.kwargs) c = fillna(c) return c
def comparator(x, y): dist = _haversine_distance(*[*x, *y]) return _exp_sim( np.float32(dist), scale=np.float32(0.1), offset=np.float32(0.01))
def _geo_internal(lat1, lng1, lat2, lng2, call_method, *args, **kw): # compute the 1D distance between the values d = _haversine_distance(lat1, lng1, lat2, lng2) return call_method(d, *args, **kw)