def convert_pot_df_to_pot(is_quantum, pot_df, ord_nodes, normalize=True):
"""
Returns a Potential pot with ordered nodes ord_nodes.
Parameters
----------
is_quantum : bool
pot_df : pandas.DataFrame
a dataframe returned by learn_pot_df()
ord_nodes : list[BayesNode]
ordered nodes of Potential pot that is returned
normalize : bool
True if want pot to be normalized as a cond probability
or cond amplitude, P(x|y) or A(x|y), where x is ord_nodes[-1]
Returns
-------
pandas.DataFrame
"""
pot = DiscreteCondPot(is_quantum, ord_nodes, bias=0)
for row in range(len(pot_df.index)):
# print([pot_df.iloc[row, col]
# for col in range(len(ord_nodes))
# ])
index = [
ord_nodes[col].st_name_index(str(pot_df.iloc[row, col]))
for col in range(len(ord_nodes))
]
pot.pot_arr[tuple(index)] = pot_df.loc[row, 'pot_values']
if normalize:
pot.normalize_self()
# print(pot)
return pot
def convert_pot_df_to_pot(is_quantum, pot_df, ord_nodes, normalize=True):
"""
Returns a Potential pot with ordered nodes ord_nodes.
Parameters
----------
is_quantum : bool
pot_df : pandas.DataFrame
a dataframe returned by learn_pot_df()
ord_nodes : list[BayesNode]
ordered nodes of Potential pot that is returned
normalize : bool
True if want pot to be normalized as a cond probability
or cond amplitude, P(x|y) or A(x|y), where x is ord_nodes[-1]
Returns
-------
pandas.DataFrame
"""
pot = DiscreteCondPot(is_quantum, ord_nodes, bias=0)
for row in range(len(pot_df.index)):
# print([pot_df.iloc[row, col]
# for col in range(len(ord_nodes))
# ])
index = [
ord_nodes[col].st_name_index(str(pot_df.iloc[row, col]))
for col in range(len(ord_nodes))
]
pot.pot_arr[tuple(index)] = pot_df.loc[row, 'pot_values']
if normalize:
pot.normalize_self()
# print(pot)
return pot
def convert_pot_df_to_pot(is_quantum,
pot_df,
ord_nodes,
s_d_pair,
use_int_sts=None,
normalize=True):
"""
Returns a DiscreteCondPot pot with ordered nodes ord_nodes.
Parameters
----------
is_quantum : bool
pot_df : pandas.DataFrame
a dataframe returned by learn_pot_df(). pot_df must have one
column for each node in ord_nodes plus additioanl final column
with pot values
ord_nodes : list[BayesNode]
ordered nodes of Potential pot that is returned. Nodes must be
ordered so that pot_df[:-1].columns = [nd.name for nd in
ord_nodes]
s_d_pair : tuple[int|str, float]
a pair consisting of a state and a degs. The state is taken
directly from states_df, so it might be a digit or an actual
state name, depending on whether use_int_sts was True or False
when states_df was generated. The state is a state of the focus
node (the last column of states_cols corresponds to the focus
node). The degs is an angle in degrees. For an s_d_pair equal to
(x0,ang), whenever pot(C=x| pa(C)=y) = 0 for all x, we will set
pot(C=x|pa(C)=y) = exp( 1j*ang*pi/180)*delta(x, x0) in the
quantum case and pot( C=x| pa(C)=y) = delta(x, x0) in the
classical case.
use_int_sts : bool
True if states in states_df are integers, False if they are the
actual state names. If the parameter use_int_sts is set to None,
this function will attempt to find its bool value using the
function NetStrucLner:int_sts_detector()
normalize : bool
True if want pot to be normalized as a cond probability
or cond amplitude, P(x|y) or A(x|y), where x is ord_nodes[-1]
Returns
-------
DiscreteCondPot
"""
for k, vtx in enumerate(pot_df.columns[:-1]):
assert ord_nodes[k].name == vtx
focus_vtx = pot_df.columns[-2]
focus_nd = ord_nodes[-1]
if use_int_sts is None:
use_int_sts = NetParamsLner.int_sts_detector(pot_df[:-1])
def int_state(st):
if use_int_sts:
return int(st)
else:
return focus_nd.pos_of_st_name(str(st))
pot = DiscreteCondPot(is_quantum, ord_nodes, bias=0)
for row in range(len(pot_df.index)):
# print([pot_df.iloc[row, col]
# for col in range(len(ord_nodes))
# ])
if not use_int_sts:
arr_index = [
ord_nodes[col].pos_of_st_name(str(pot_df.iloc[row, col]))
for col in range(len(ord_nodes))
]
else:
arr_index = [
int(pot_df.iloc[row, col]) for col in range(len(ord_nodes))
]
pot.pot_arr[tuple(arr_index)] = \
pot_df.loc[row, 'last_col_with_vals']
if normalize:
try:
pot.normalize_self()
except UnNormalizablePot as xce:
# print('caught exception')
focus_index = int_state(s_d_pair[0])
arr_index = tuple(list(xce.pa_indices) + [focus_index])
# print('********************arr_index', arr_index)
if is_quantum:
pot.pot_arr[arr_index] = \
np.exp(1j * s_d_pair[1] * np.pi / 180)
else:
pot.pot_arr[arr_index] = 1.0
print('mended pot:\n', pot)
# try normalizing pot again now that it is mended
pot.normalize_self()
return pot
def convert_pot_df_to_pot(is_quantum, pot_df, ord_nodes,
s_d_pair, use_int_sts=None, normalize=True):
"""
Returns a DiscreteCondPot pot with ordered nodes ord_nodes.
Parameters
----------
is_quantum : bool
pot_df : pandas.DataFrame
a dataframe returned by learn_pot_df(). pot_df must have one
column for each node in ord_nodes plus additioanl final column
with pot values
ord_nodes : list[BayesNode]
ordered nodes of Potential pot that is returned. Nodes must be
ordered so that pot_df[:-1].columns = [nd.name for nd in
ord_nodes]
s_d_pair : tuple[int|str, float]
a pair consisting of a state and a degs. The state is taken
directly from states_df, so it might be a digit or an actual
state name, depending on whether use_int_sts was True or False
when states_df was generated. The state is a state of the focus
node (the last column of states_cols corresponds to the focus
node). The degs is an angle in degrees. For an s_d_pair equal to
(x0,ang), whenever pot(C=x| pa(C)=y) = 0 for all x, we will set
pot(C=x|pa(C)=y) = exp( 1j*ang*pi/180)*delta(x, x0) in the
quantum case and pot( C=x| pa(C)=y) = delta(x, x0) in the
classical case.
use_int_sts : bool
True if states in states_df are integers, False if they are the
actual state names. If the parameter use_int_sts is set to None,
this function will attempt to find its bool value using the
function NetStrucLner:int_sts_detector()
normalize : bool
True if want pot to be normalized as a cond probability
or cond amplitude, P(x|y) or A(x|y), where x is ord_nodes[-1]
Returns
-------
DiscreteCondPot
"""
for k, vtx in enumerate(pot_df.columns[:-1]):
assert ord_nodes[k].name == vtx
focus_vtx = pot_df.columns[-2]
focus_nd = ord_nodes[-1]
if use_int_sts is None:
use_int_sts = NetStrucLner.int_sts_detector(pot_df[:-1])
def int_state(st):
if use_int_sts:
return int(st)
else:
return focus_nd.st_name_index(str(st))
pot = DiscreteCondPot(is_quantum, ord_nodes, bias=0)
for row in range(len(pot_df.index)):
# print([pot_df.iloc[row, col]
# for col in range(len(ord_nodes))
# ])
if not use_int_sts:
arr_index = [
ord_nodes[col].st_name_index(str(pot_df.iloc[row, col]))
for col in range(len(ord_nodes))]
else:
arr_index = [int(pot_df.iloc[row, col])
for col in range(len(ord_nodes))]
pot.pot_arr[tuple(arr_index)] = \
pot_df.loc[row, 'last_col_with_vals']
if normalize:
try:
pot.normalize_self()
except UnNormalizablePot as xce:
# print('caught exception')
focus_index = int_state(s_d_pair[0])
arr_index = tuple(list(xce.pa_indices) + [focus_index])
# print('********************arr_index', arr_index)
if is_quantum:
pot.pot_arr[arr_index] = \
np.exp(1j * s_d_pair[1] * np.pi / 180)
else:
pot.pot_arr[arr_index] = 1.0
print('mended pot:\n', pot)
# try normalizing pot again now that it is mended
pot.normalize_self()
return pot
