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gridminmax.py
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gridminmax.py
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'''
Created on Nov 27, 2017
@author: fan
'''
import numpy as np
import scipy.stats
import pyfan.amto.array.mesh as mesh
def three_vec_grids(vara_min, vara_max, vara_grid, vara_grid_add=None,
varb_min=None, varb_max=None, varb_grid=None, varb_grid_add=None,
varc_min=None, varc_max=None, varc_grid=None, varc_grid_add=None,
gridtype='grid', tomesh=False,
return_joint=False, return_single_col=False,
seed=999):
"""Grid for VFI
Temporary code, so that I can deal with minimal school hour. should be
deleted in the future. and combined with the method above
"""
if (gridtype == 'grid'):
a = np.linspace(vara_min, vara_max, vara_grid)
if (varb_grid is not None):
b = np.linspace(varb_min, varb_max, varb_grid)
if (varc_grid is not None):
c = np.linspace(varc_min, varc_max, varc_grid)
if (varc_grid_add is not None):
c = np.append(c, varc_grid_add)
if (gridtype == 'rand'):
np.random.seed(seed)
a = random_vector_min_max(vara_min, vara_max, vara_grid)
if (varb_grid is not None):
b = random_vector_min_max(varb_min, varb_max, varb_grid)
if (varc_grid is not None):
c = random_vector_min_max(varc_min, varc_max, varc_grid)
if (vara_grid_add is not None):
a = np.append(a, vara_grid_add)
if (varb_grid_add is not None):
b = np.append(b, varb_grid_add)
if (varc_grid_add is not None):
c = np.append(c, varc_grid_add)
a = np.sort(a)
a = np.reshape(a, (-1, 1))
if (varb_grid is not None):
b = np.sort(b)
if (varb_grid is not None):
b = np.reshape(b, (-1, 1))
if (varc_grid is not None):
c = np.sort(c)
if (varc_grid is not None):
c = np.reshape(c, (-1, 1))
if (varb_grid is not None):
if (varc_grid is not None):
if (tomesh == True):
if (return_joint is True):
abc = mesh.three_mat_mesh(a, b, c,
return_joint=return_joint,
return_single_col=return_single_col)
return abc
else:
a, b, c = mesh.three_mat_mesh(a, b, c,
return_joint=return_joint,
return_single_col=return_single_col)
return a, b, c
else:
return a, b, c
else:
if (tomesh == True):
if (return_joint is True):
ab = mesh.two_mat_mesh(a, b, return_joint,
return_single_col=return_single_col)
return ab
else:
a, b = mesh.two_mat_mesh(a, b, return_joint,
return_single_col=return_single_col)
return a, b
else:
return a, b
else:
return a
def random_vector_mean_sd(mean, sd, grid_count, gridtype='grid', seed=382):
if (gridtype == 'grid'):
unif_grid = np.linspace(0.001, 0.999, grid_count)
standard_normal_grid = scipy.stats.norm.ppf(unif_grid)
standard_normal_grid = np.reshape(standard_normal_grid, (-1, 1))
if (gridtype == 'rand'):
np.random.seed(seed)
standard_normal_grid = np.random.randn(grid_count, 1)
normal_grid = mean + sd * standard_normal_grid
return normal_grid
def random_vector_min_max(minval, maxval, grid_count):
if (grid_count == 1):
'grid_count == 1 means we have no choice along that dimension'
random_points = np.random.uniform(grid_count)
random_vector = minval + random_points * (maxval - minval)
else:
random_points = np.random.uniform(size=(grid_count - 2))
random_vector = minval + random_points * (maxval - minval)
random_vector = np.insert(random_vector, 0, minval)
random_vector = np.insert(random_vector, len(random_vector), maxval)
return random_vector
if __name__ == '__main__':
vara_min = 1
vara_max = 10
vara_grid = 4
varb_min = 30
varb_max = 31
varb_grid = 2
varc_min = -10
varc_max = -5
varc_grid = 3
print('3 grids, 3 outputs')
[veca, vecb, vecc] = three_vec_grids(vara_min, vara_max, vara_grid,
vara_grid_add=None,
varb_min=varb_min, varb_max=varb_max, varb_grid=varb_grid,
varb_grid_add=None,
varc_min=varc_min, varc_max=varc_max, varc_grid=varc_grid,
varc_grid_add=None,
gridtype='grid', tomesh=False, return_joint=False, seed=999)
print('vec a:', np.transpose(veca))
print('vec b:', np.transpose(vecb))
print('vec c:', np.transpose(vecc))
print('3 grids, mesh, 3 outputs')
[veca, vecb, vecc] = three_vec_grids(vara_min, vara_max, vara_grid,
vara_grid_add=None,
varb_min=varb_min, varb_max=varb_max, varb_grid=varb_grid,
varb_grid_add=None,
varc_min=varc_min, varc_max=varc_max, varc_grid=varc_grid,
varc_grid_add=None,
gridtype='grid', tomesh=True, return_joint=False, seed=999)
print('vec a:', np.transpose(veca))
print('vec b:', np.transpose(vecb))
print('vec c:', np.transpose(vecc))
print('3 grids, mesh, joint')
outputs = three_vec_grids(vara_min, vara_max, vara_grid,
vara_grid_add=None,
varb_min=varb_min, varb_max=varb_max, varb_grid=varb_grid,
varb_grid_add=None,
varc_min=varc_min, varc_max=varc_max, varc_grid=varc_grid,
varc_grid_add=None,
gridtype='grid', tomesh=True, return_joint=True, seed=999)
print('outputs:', outputs)
print('3 grids, mesh, joint, rand')
outputs = three_vec_grids(vara_min, vara_max, vara_grid,
vara_grid_add=None,
varb_min=varb_min, varb_max=varb_max, varb_grid=varb_grid,
varb_grid_add=None,
varc_min=varc_min, varc_max=varc_max, varc_grid=varc_grid,
varc_grid_add=None,
gridtype='rand', tomesh=True, return_joint=True, seed=999)
print('outputs:', outputs)
print('3 grids, mesh, joint, rand, add_more')
outputs = three_vec_grids(vara_min, vara_max, vara_grid,
vara_grid_add=np.array([-1]),
varb_min=varb_min, varb_max=varb_max, varb_grid=varb_grid,
varb_grid_add=None,
varc_min=varc_min, varc_max=varc_max, varc_grid=varc_grid,
varc_grid_add=np.array([-999, -99, 111]),
gridtype='rand', tomesh=True, return_joint=True, seed=999)
print('outputs:', outputs)
print('2 grids, mesh, joint, rand, add_more')
outputs = three_vec_grids(vara_min, vara_max, vara_grid,
vara_grid_add=np.array([-1, 839, 33]),
varb_min=varb_min, varb_max=varb_max, varb_grid=varb_grid,
varb_grid_add=None,
gridtype='grid', tomesh=True, return_joint=True, seed=999)
print('outputs:', outputs)