def move(self, model, board, temp=1.0, dirichlet=True): #Given a python-chess board, give a normal move
turn = board.turn
k_castle = board.has_kingside_castling_rights(turn)
q_castle = board.has_queenside_castling_rights(turn)
kb_castle = board.has_kingside_castling_rights(not turn)
qb_castle = board.has_queenside_castling_rights(not turn)
check = board.is_check()
position = helper.conversion(str(board), turn, k_castle, q_castle, check, kb_castle, qb_castle)
#We will be exploring the statespace using only the policy-head
policy, _ = model.predict(position)
policy = policy.reshape(1858, 1)
move = helper.policy_to_move(policy, board, dirichlet=dirichlet, temp=temp)
#Push move now
try:
board.push_uci(move)
except:
print("Error with move")
for legal_move in board.legal_moves:
board.push(legal_move)
break
def test_codeblock_attributes():
doc = conversion(
'''
::: {latex-admonition-color=xyz latex-admonition-linewidth=xyz' latex-admonition-margin=xyz latex-admonition-innermargin=xyz latex-admonition-localfootnotes=true} :::
:::::::::
''', 'latex')
text = convert_text(doc,
input_format='panflute',
output_format='latex',
extra_args=['--wrap=none'])
assert '\\begin{env-' in text
assert '\\end{env-' in text
assert pandoc_latex_admonition.environment_option(
'left', 2, 5, -4, 'black') in doc.get_metadata()['header-includes'][-1]
def main():
# read in data
data = pd.read_csv("cldf/data.csv")
data = data.sort_values(
by=['var_id']) # ensure data is ordered by line for future filtering
coding_conversions = pd.read_csv("etc/codes.csv")
# create new dataframe to fill in
new_data = pd.DataFrame(
columns=["song_id", "society_id", "var_id", "code", "ID"])
# for logging
line_lag = "line_0"
for index, row in data.iterrows():
split_df = pd.DataFrame(
columns=["song_id", "society_id", "var_id", "code", "ID"])
line = row['var_id']
if (pd.isnull(row["code"])):
print(line + " - " + str(row["song_id"]) + " - " +
str(row["society_id"]) + " is missing a value")
continue
# Data was ordered in line order, so we only need the line set once.
# Print the line being analysed for logging purposes
if (line_lag != line):
print("Recoding " + line + "...")
line_lag = line
value_set = coding_conversions.loc[coding_conversions["var_id"] ==
line, "code"].to_numpy()
value = row['code']
new_codes = conversion(value, value_set)
## If new codes are -99 then they were invalid codes and we need to correct them
if (new_codes[0] == -99):
with open("logs/invalid_codes.txt", "a") as invalid_file:
invalid_file.write(line + " - " + str(row["song_id"]) + " - " +
str(row["society_id"]) +
" is an invalid code\n")
split_df = split_df.append([row] * len(new_codes), ignore_index=True)
split_df['code'] = new_codes
new_data = new_data.append(split_df)
# save file
new_data.to_csv("cldf/data.csv", index=False)
def test_beamer_notes():
doc = conversion(
'''
::: {latex-admonition-color=black} :::
This a text[^note]
[^note]: This is a *note*
:::
''', 'beamer')
text = convert_text(doc,
input_format='panflute',
output_format='beamer',
extra_args=['--wrap=none'])
assert 'This a text\\footnote<.->[frame]{This is a \\emph{note}}' in text
def play_from(
board, positions, moves, values, lock
): #Possible improvement could be placing our data into tuples?? post-processing required tho
import chess
import chess.uci
import numpy as np
from helper import conversion, one_hot
stockfish = chess.uci.popen_engine("stockfish")
info_handler = chess.uci.InfoHandler()
stockfish.info_handlers.append(info_handler)
stockfish.uci()
while not board.is_game_over():
stockfish.position(board)
stock_move = stockfish.go(
movetime=250) #Set stockfish to 5 moves per second
#Generate position
turn = board.turn
k_castle = board.has_kingside_castling_rights(turn)
q_castle = board.has_queenside_castling_rights(turn)
kb_castle = board.has_kingside_castling_rights(not turn)
qb_castle = board.has_queenside_castling_rights(not turn)
check = board.is_check()
position = conversion(str(board), turn, k_castle, q_castle, check,
kb_castle,
qb_castle) #Append position to training data
mate = info_handler.info["score"][1].mate
#Acquire lock so that all data is appended to our lists in the correct order!
lock.acquire()
positions.append(position)
moves.append(one_hot(
stock_move[0])) #Add stockfish's choice to training data
if mate is None:
value = np.tanh(info_handler.info["score"][1].cp / 300)
values.append(value)
else:
values.append(mate / np.abs(mate))
lock.release()
board.push(stock_move[0])
def test_latex_images():
doc = conversion(
'''
::: {latex-admonition-color=black} :::
![Title]()
Hello
:::::::::
''', 'latex')
text = convert_text(doc,
input_format='panflute',
output_format='latex',
extra_args=['--wrap=none'])
assert '\\begin{env-' in text
assert '\\end{env-' in text
assert pandoc_latex_admonition.environment_option(
'left', 2, 5, -4, 'black') in doc.get_metadata()['header-includes'][-1]
assert isinstance(doc.content[-1], Para)
assert isinstance(doc.content[-1].content[0], Image)
def test_conversion():
# single codings
print("singles")
single_1 = (conversion(2**1, [1, 4, 7, 10, 13])) == (1, )
single_2 = (conversion(2, [1, 4, 7, 10, 13])) == (1, )
single_3 = (conversion(2**10, [1, 4, 7, 10, 13])) == (10, )
print("twos")
# two codings
double_1 = (conversion(20, [4, 2, 1])) == (4, 2) # 4 , 2
double_2 = (conversion(1028, [4, 2, 10])) == (2, 10) # 10, 2
print('threes')
# # # three codings
triple_1 = (conversion(8336, [13, 7, 4, 2, 6])) == (13, 7, 4) # 13, 7 , 4
triple_2 = (conversion(8464, [13, 8, 4, 2, 6])) == (13, 8, 4) # 13, 8, 4
assert all(
[single_1, single_2, single_3, double_1, double_2, triple_1, triple_2])
def test_codeblock_classes():
doc = conversion(
'''
---
pandoc-latex-admonition:
- classes: ['class1', 'class2']
color: red
position: right
linewidth: 5
innermargin: -8
margin: 10
---
~~~{.class1 .class2}
~~~
''', 'latex')
text = convert_text(doc,
input_format='panflute',
output_format='latex',
extra_args=['--wrap=none'])
assert '\\begin{env-' in text
assert '\\end{env-' in text
assert pandoc_latex_admonition.environment_option(
'right', 5, -8, 10, 'red') in doc.get_metadata()['header-includes'][-1]