def start_battle(browser, tree):
# Load the poke objects
poke_dict = load_pokemon()
move_dict = load_moves()
wait_load(browser, "chooseTeamPreview")
battle_string = get_battle_log(browser)
current, lines, p1_poke, p2_poke = pre_search.get_pre_battle(battle_string) # Get pre-battle info
populate_pokes(p1_poke, p2_poke, poke_dict) # Compute unknown stats from Smogon EVs
start = -1
current_mon = [None, None]
while True:
state = get_move_options(browser)
append_string = get_battle_log(browser)
if start < 1:
if start == 0:
current, current_mon = get_starting_mon(0, append_string.split('\n'))
start += 1
else:
p1_poke, p2_poke, current_mon = simulate_turn(append_string, current_mon, p1_poke, p2_poke)
if state == "selectMove":
arr, options = get_game_state(current_mon, p1_poke, p2_poke, move_dict, poke_dict, False)
arr = move_tree.convert_numeric(arr)
node = dtree_build.classify(arr, tree) # Classify the row
l = get_class_list(node)
move = recommend_move(l, options, p2_poke[current_mon[1]], move_dict, p2_poke, poke_dict)
print(move)
if move == p2_poke[current_mon[1]].name or move is None:
click_button(browser, "selectMove")
click_button(browser, "chooseMove")
else:
use_move(move, browser, move_dict)
elif state == "chooseSwitch":
arr, options = get_game_state(current_mon, p1_poke, p2_poke, move_dict, poke_dict, True)
arr = move_tree.convert_numeric(arr)
node = dtree_build.classify(arr, tree) # Classify the row
l = get_class_list(node)
move = recommend_move(l, options, p2_poke[current_mon[1]], move_dict, p2_poke, poke_dict)
print(move)
use_move(move, browser, move_dict)
elif state == "chooseTeamPreview":
click_button(browser, "chooseTeamPreview")
def main(col_names=None):
if len(sys.argv) < 2:
print("Please specify input csv file name")
return
csv_file_name = sys.argv[1]
data = []
with open(csv_file_name) as csvfile:
readCSV = csv.reader(csvfile, delimiter=',')
for row in readCSV:
data.append(list(row))
train = resample(data[1:], replace=True, n_samples=int(len(data)))
test = []
for i in data[1:]:
if i not in train:
test.append(i)
tree = dtree_build.buildtree(train, min_gain=0.01, min_samples=5)
dtree_build.printtree(tree, '', col_names)
result2 = naive_bayes.build(train)
# print(result2)
# max_tree_depth = dtree_build.max_depth(tree)
# print("max number of questions=" + str(max_tree_depth))
# print(test)
out_put = [['instance', 'actual', 'predicted', 'probability']]
total = 0
correct = 0
correct2 = 0
for i in test:
total += 1
result = dtree_build.classify(i, tree)
out = naive_bayes.classifier(result2, i)
sum_probability = 0
max_number = 0
choice = ''
for n, m in result.items():
sum_probability += m
if m >= max_number:
max_number = m
choice = n
if choice == i[-1]:
correct += 1
if out == int(i[-1]):
correct2 += 1
sublist = [total, i[-1], choice, max_number / sum_probability]
out_put.append(sublist)
# print(result)
with open("predicted.csv", "w") as output:
writer = csv.writer(output)
writer.writerows(out_put)
print("Accuracy for decision tree is", correct / len(test))
print("Accuracy for naive bayes is", correct2 / len(test))
def classify_pokemon(tree, test_f, output_f):
entries = tsv.get_list(test_f)
output = open(output_f, "w")
for entry in entries:
poke = entry.pop(0)
entry = move_tree.convert_numeric(entry)
node = dtree_build.classify(entry, tree) # Classify the row
poke_class = ""
for k in node: # There are no non-pure nodes, so this works.
poke_class = k
output.write(poke+ '\t' + poke_class + '\n') # Store as (move name, class) in .tsv
def classify_moves(tree, test_f, output_f):
test_file = open(test_f)
output = open(output_f, "w")
for line in test_file:
arr = line.rstrip().split('\t')
move_name = arr.pop(0)
entry = convert_numeric(arr)
node = dtree_build.classify(entry, tree) # Classify the row
move_class = ""
for k in node: # There are no non-pure nodes, so this works.
move_class = k
output.write(move_name + '\t' + move_class + '\n') # Store as (move name, class) in .tsv
test_file.close()
output.close()
def main(col_names=None):
# parse command-line arguments to read the name of the input csv file
# and optional 'draw tree' parameter
if len(sys.argv) < 2: # input file name should be specified
print ("Please specify input csv file name")
return
csv_file_name = sys.argv[1]
data = []
with open(csv_file_name) as csvfile:
readCSV = csv.reader(csvfile, delimiter=',')
for row in readCSV:
list = []
for attribute in row:
try:
list += [float(attribute)]
except:
list += [attribute]
data.append(list)
print("Total number of records = ",len(data))
tree = regression_tree.buildtree(data, min_gain = 0.005, min_samples = 5)
regression_tree.printtree(tree, '', col_names)
max_tree_depth = dtree_build.max_depth(tree)
print("max number of questions=" + str(max_tree_depth))
if len(sys.argv) > 2: # draw option specified
import regression_draw
regression_draw.drawtree(tree, jpeg=csv_file_name+'.jpg')
if len(sys.argv) > 3: # create json file for d3.js visualization
import json
import dtree_to_json
json_tree = dtree_to_json.dtree_to_jsontree(tree, col_names)
print(json_tree)
# create json data for d3.js interactive visualization
with open(csv_file_name + ".json", "w") as write_file:
json.dump(json_tree, write_file)
print("course ['teaching', 'not minority', 'female', 'english', 50, 30, 'lower', 7, 4] is: ", dtree_build.classify(['teaching', 'not minority', 'female', 'english', 50, 30, 'lower', 7, 4], tree))
print("course ['teaching', 'not minority', 'male', 'english', 40, 30, 'lower', 6, 4] is: ", dtree_build.classify(['teaching', 'not minority', 'male', 'english', 40, 30, 'lower', 6, 4], tree))
print("course ['teaching', 'not minority', 'male', 'english', 70, 30, 'lower', 4, 4] is: ", dtree_build.classify(['teaching', 'not minority', 'male', 'english', 70, 30, 'lower', 4, 4], tree))
import dtree_build
import sys
if __name__ == "__main__":
# fruits with their size and color
fruits = [[4, 'red', 'apple'], [4, 'green', 'apple'], [1, 'red', 'cherry'],
[1, 'green', 'grape'], [5, 'red', 'apple']]
tree = dtree_build.buildtree(fruits)
dtree_build.printtree(tree, '', ["size", "color"])
print("fruit [2, 'red'] is: ", dtree_build.classify([2, 'red'], tree))
print("fruit [4.5, 'red'] is: ", dtree_build.classify([4.5, 'red'], tree))
print("fruit [1.4, 'green'] is: ",
dtree_build.classify([1.4, 'green'], tree))
max_tree_depth = dtree_build.max_depth(tree)
print("max number of questions=" + str(max_tree_depth))
if len(sys.argv) > 1: # draw option specified
import dtree_draw
dtree_draw.drawtree(tree, jpeg='fruits_dt.jpg')