def join(cls, *grids):
keys = {}
for g in grids:
for k in g.iterkeys():
if k not in keys:
keys[k] = 0
keys[k] += 1
for k, v in keys.iteritems():
if v > 1:
raise ValueError(
"Grids cannot be joined, they shares some keys: %s" % k)
kv = [g._getKeysValues() for g in grids]
keys = [i[0] for i in kv]
values = [i[1] for i in kv]
new_keys = []
[new_keys.extend(i) for i in keys]
new_values = []
values = _cartezianProduct(*values)
for i in values:
value = []
for j in i:
value.extend(j)
new_values.append(value)
d = {}
for value in new_values:
for k, v in zip(new_keys, value):
if k not in d:
d[k] = []
d[k].append(v)
return cls(d)
def join(cls, *grids):
keys = {}
for g in grids:
for k in g.iterkeys():
if k not in keys:
keys[k] = 0
keys[k] += 1
for k, v in keys.iteritems():
if v > 1:
raise ValueError("Grids cannot be joined, they shares some keys: %s" % k)
kv = [g._getKeysValues() for g in grids]
keys = [i[0] for i in kv]
values = [i[1] for i in kv]
new_keys = []
[new_keys.extend(i) for i in keys]
new_values = []
values = _cartezianProduct(*values)
for i in values:
value = []
for j in i:
value.extend(j)
new_values.append(value)
d = {}
for value in new_values:
for k, v in zip(new_keys, value):
if k not in d:
d[k] = []
d[k].append(v)
return cls(d)
def cartezian(cls, *args, **kwargs):
"""Construct tuning grid from required parameter values (Cartesian product)
It will construct tuning grid from `grid` parameter. `grid` is a
dictionary with parameter names as keys and required values of this
parameter as values. Resulting tuning grid is constructed as Cartesian
product of parameter values.
"""
grid = dict(*args, **kwargs).items()
grid_keys = [k for k, v in grid]
grid_values = [v for k, v in grid]
grid_values = _cartezianProduct(*grid_values)
ret = cls((k, []) for k in grid_keys)
for item in grid_values:
for key, value in zip(grid_keys, item):
ret[key].append(value)
ret._criterion = [None] * len(grid_values)
return ret
def cartezianGrid(cls, *args, **kwargs):
"""Construct tuning grid from required parameter values (Cartesian product)
It will construct tuning grid from `grid` parameter. `grid` is a
dictionary with parameter names as keys and required values of this
parameter as values. Resulting tuning grid is constructed as Cartesian
product of parameter values.
"""
grid = dict(*args, **kwargs).items()
grid_keys = [k for k, v in grid]
grid_values = [v for k, v in grid]
grid_values = _cartezianProduct(*grid_values)
ret = cls((k, []) for k in grid_keys)
for item in grid_values:
for key, value in zip(grid_keys, item):
ret[key].append(value)
ret._criterion = [None] * len(grid_values)
return ret
