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)