
import numpy as np
import scipy as sp

import mdptoolbox.example

STATES = 10
ACTIONS = 3
SMALLNUM = 10e-12

# np.arrays
P_small = np.array([[[0.5, 0.5],[0.8, 0.2]],[[0, 1],[0.1, 0.9]]])
R_small = np.array([[5, 10], [-1, 2]])

P_sparse = np.empty(2, dtype=object)
P_sparse[0] = sp.sparse.csr_matrix([[0.5, 0.5],[0.8, 0.2]])
P_sparse[1] = sp.sparse.csr_matrix([[0, 1],[0.1, 0.9]])

P_forest, R_forest = mdptoolbox.example.forest()

P_forest_sparse, R_forest_sparse = mdptoolbox.example.forest(S=STATES,
                                                             is_sparse=True)

np.random.seed(0)
P_rand, R_rand = mdptoolbox.example.rand(STATES, ACTIONS)

np.random.seed(0)
P_rand_sparse, R_rand_sparse = mdptoolbox.example.rand(STATES, ACTIONS,
                                                       is_sparse=True)


import numpy as np
import scipy as sp

import mdptoolbox.example

STATES = 10
ACTIONS = 3
SMALLNUM = 10e-12

# np.arrays
P_small, R_small = mdptoolbox.example.small()

P_sparse = np.empty(2, dtype=object)
P_sparse[0] = sp.sparse.csr_matrix(P_small[0])
P_sparse[1] = sp.sparse.csr_matrix(P_small[1])

P_forest, R_forest = mdptoolbox.example.forest()

P_forest_sparse, R_forest_sparse = mdptoolbox.example.forest(S=STATES,
                                                             is_sparse=True)

np.random.seed(0)
P_rand, R_rand = mdptoolbox.example.rand(STATES, ACTIONS)

np.random.seed(0)
P_rand_sparse, R_rand_sparse = mdptoolbox.example.rand(STATES, ACTIONS,
                                                       is_sparse=True)

Use mdptoolbox.example.small in the tests
[tests] Use mdptoolbox.example.small in the tests

  
  import numpy as np
  import scipy as sp
  
  import mdptoolbox.example
  
  STATES = 10
  ACTIONS = 3
  SMALLNUM = 10e-12
  
  # np.arrays
+ P_small, R_small = mdptoolbox.example.small()
- P_small = np.array([[[0.5, 0.5],[0.8, 0.2]],[[0, 1],[0.1, 0.9]]])
- R_small = np.array([[5, 10], [-1, 2]])
  
  P_sparse = np.empty(2, dtype=object)
- P_sparse[0] = sp.sparse.csr_matrix([[0.5, 0.5],[0.8, 0.2]])
+ P_sparse[0] = sp.sparse.csr_matrix(P_small[0])
- P_sparse[1] = sp.sparse.csr_matrix([[0, 1],[0.1, 0.9]])
+ P_sparse[1] = sp.sparse.csr_matrix(P_small[1])
  
  P_forest, R_forest = mdptoolbox.example.forest()
  
  P_forest_sparse, R_forest_sparse = mdptoolbox.example.forest(S=STATES,
                                                               is_sparse=True)
  
  np.random.seed(0)
  P_rand, R_rand = mdptoolbox.example.rand(STATES, ACTIONS)
  
  np.random.seed(0)
  P_rand_sparse, R_rand_sparse = mdptoolbox.example.rand(STATES, ACTIONS,
                                                         is_sparse=True)
  
Use mdptoolbox.example.small in the tests
## Code Before:

import numpy as np
import scipy as sp

import mdptoolbox.example

STATES = 10
ACTIONS = 3
SMALLNUM = 10e-12

# np.arrays
P_small = np.array([[[0.5, 0.5],[0.8, 0.2]],[[0, 1],[0.1, 0.9]]])
R_small = np.array([[5, 10], [-1, 2]])

P_sparse = np.empty(2, dtype=object)
P_sparse[0] = sp.sparse.csr_matrix([[0.5, 0.5],[0.8, 0.2]])
P_sparse[1] = sp.sparse.csr_matrix([[0, 1],[0.1, 0.9]])

P_forest, R_forest = mdptoolbox.example.forest()

P_forest_sparse, R_forest_sparse = mdptoolbox.example.forest(S=STATES,
                                                             is_sparse=True)

np.random.seed(0)
P_rand, R_rand = mdptoolbox.example.rand(STATES, ACTIONS)

np.random.seed(0)
P_rand_sparse, R_rand_sparse = mdptoolbox.example.rand(STATES, ACTIONS,
                                                       is_sparse=True)

## Instruction:
Use mdptoolbox.example.small in the tests
## Code After:

import numpy as np
import scipy as sp

import mdptoolbox.example

STATES = 10
ACTIONS = 3
SMALLNUM = 10e-12

# np.arrays
P_small, R_small = mdptoolbox.example.small()

P_sparse = np.empty(2, dtype=object)
P_sparse[0] = sp.sparse.csr_matrix(P_small[0])
P_sparse[1] = sp.sparse.csr_matrix(P_small[1])

P_forest, R_forest = mdptoolbox.example.forest()

P_forest_sparse, R_forest_sparse = mdptoolbox.example.forest(S=STATES,
                                                             is_sparse=True)

np.random.seed(0)
P_rand, R_rand = mdptoolbox.example.rand(STATES, ACTIONS)

np.random.seed(0)
P_rand_sparse, R_rand_sparse = mdptoolbox.example.rand(STATES, ACTIONS,
                                                       is_sparse=True)
