def tolerance(lower_bounds, upper_bounds, data=None):
""" Returns a new, single point some tolerence away from existing points.
Parameters
----------
lower_bounds : list
List of ``float`` that are lower bounds. List is indexed by parameter.
upper_bounds : list
List of ``float`` that are upper bounds. List is indexed by parameter.
data : {None, list}
List of tuples. Each tuple is a list of ``float`` that are previous
data points.
Returns
-------
pts : numpy.array
An array with new point.
"""
rtol = None
dist = None
n_new_pts = 1
data = [] if data == None else data
if len(data) == 0:
return uniform(lower_bounds, upper_bounds)
pts = math.fillpts(lower_bounds, upper_bounds, n_new_pts, data, rtol, dist)
return pts[0]
def _InitialPoints(self): #XXX: user can provide legacy data?
"""Generate a grid of starting points for the ensemble of optimizers"""
npts = self._npts
if len(self._strictMax): upper = list(self._strictMax)
else:
upper = list(self._defaultMax)
if len(self._strictMin): lower = list(self._strictMin)
else:
lower = list(self._defaultMin)
# build a grid of starting points
from mystic.math import fillpts
data = self._evalmon._x if self._evalmon else []
data += self._stepmon._x if self._stepmon else []
return fillpts(lower, upper, npts, data, self._rtol, self._dist)
data=[[0,0]]
#data = []
ndim = len(data[0]) if data else 2
lb,ub = [-10]*ndim, [10]*ndim
npts = 12
rtol = None # 4.5
#from mystic.math import Distribution
#from scipy.stats import norm
dist = None # Distribution(norm,0,2)
if __name__ == '__main__':
from mystic.math import fillpts
pts = fillpts(lb, ub, npts, data, rtol, dist)
import numpy as np
print(np.array(pts).reshape(-1,ndim))
import matplotlib.pyplot as plt
_x,_y = np.array(pts).reshape(-1,ndim).T
x,y = np.array(data).reshape(-1,ndim).T
plt.scatter(x=x,y=y)
plt.scatter(x=_x,y=_y)
plt.show()
#data=[[2,-6],[5,-7],[1,0],[1,3],[5,9],[2,2],[-10,-10],[-10,10],[10,-10]]
#data=[[0,0],[-10,-10],[-10,0],[10,0],[0,-10],[0,10],[10,-10],[-10,10],[10,10]]
data = [[0, 0]]
#data = []
ndim = len(data[0]) if data else 2
lb, ub = [-10] * ndim, [10] * ndim
npts = 12
rtol = None # 4.5
#from mystic.math import Distribution
#from scipy.stats import norm
dist = None # Distribution(norm,0,2)
if __name__ == '__main__':
from mystic.math import fillpts
pts = fillpts(lb, ub, npts, data, rtol, dist)
import numpy as np
print(np.array(pts).reshape(-1, ndim))
import matplotlib.pyplot as plt
_x, _y = np.array(pts).reshape(-1, ndim).T
x, y = np.array(data).reshape(-1, ndim).T
plt.scatter(x=x, y=y)
plt.scatter(x=_x, y=_y)
plt.show()
