import numpy as np
from .. import utility
MAX_SEARCH = int(30000)
[ドキュメント]class history:
def __init__(self):
self.num_runs = int(0)
self.total_num_search = int(0)
self.fx = np.zeros(MAX_SEARCH, dtype=float)
self.chosed_actions = np.zeros(MAX_SEARCH, dtype=int)
self.terminal_num_run = np.zeros(MAX_SEARCH, dtype=int)
[ドキュメント] def write(self, t, action):
"""
Overwrite fx and chosed_actions by t and action.
Parameters
----------
t: numpy.ndarray
N dimensional array. The negative energy of each search candidate (value of the objective function to be optimized).
action: numpy.ndarray
N dimensional array. The indexes of actions of each search candidate.
Returns
-------
"""
N = utility.length_vector(t)
st = self.total_num_search
en = st + N
self.terminal_num_run[self.num_runs] = en
self.fx[st:en] = t
self.chosed_actions[st:en] = action
self.num_runs += 1
self.total_num_search += N
[ドキュメント] def export_sequence_best_fx(self):
"""
Export fx and actions at each sequence.
(The total number of data is num_runs.)
Returns
-------
best_fx: numpy.ndarray
best_actions: numpy.ndarray
"""
best_fx = np.zeros(self.num_runs)
best_actions = np.zeros(self.num_runs)
for n in xrange(self.num_runs):
index = np.argmax(self.fx[0:self.terminal_num_run[n]])
best_actions[n] = self.chosed_actions[index]
best_fx[n] = self.fx[index]
return best_fx, best_actions
[ドキュメント] def export_all_sequence_best_fx(self):
"""
Export all fx and actions at each sequence.
(The total number of data is total_num_research.)
Returns
-------
best_fx: numpy.ndarray
best_actions: numpy.ndarray
"""
best_fx = np.zeros(self.total_num_search)
best_actions = np.zeros(self.total_num_search)
best_fx[0] = self.fx[0]
best_actions[0] = self.chosed_actions[0]
for n in xrange(1, self.total_num_search):
if best_fx[n-1] < self.fx[n]:
best_fx[n] = self.fx[n]
best_actions[n] = self.chosed_actions[n]
else:
best_fx[n] = best_fx[n-1]
best_actions[n] = best_actions[n-1]
return best_fx, best_actions
[ドキュメント] def save(self, filename):
"""
Save the information of the history.
Parameters
----------
filename: str
The name of the file which stores the information of the history
Returns
-------
"""
N = self.total_num_search
M = self.num_runs
np.savez_compressed(filename, num_runs=M, total_num_search=N,
fx=self.fx[0:N],
chosed_actions=self.chosed_actions[0:N],
terminal_num_run=self.terminal_num_run[0:M])
[ドキュメント] def load(self, filename):
"""
Load the information of the history.
Parameters
----------
filename: str
The name of the file which stores the information of the history
Returns
-------
"""
data = np.load(filename)
M = data['num_runs']
N = data['total_num_search']
self.num_runs = M
self.total_num_search = N
self.fx[0:N] = data['fx']
self.chosed_actions[0:N] = data['chosed_actions']
self.terminal_num_run[0:M] = data['terminal_num_run']