def __init__(self, bzrc):
self.bzrc = bzrc
self.constants = self.bzrc.get_constants()
self.commands = []
self.ALPHA = 0.01
self.BETA = 0.3
self.OBS_TOLERANCE = 35.0
self.S = 50
self.wroteonce = False
self.goalradius = 30
self.tankradius = 5
self.avoidradius = 50
self.avoidBETA = 0.05
self.aimtolerance = math.pi / 20
self.world_grid = WorldGrid()
self.bayes = Bayes()
self.turnprob = 0.05
self.turndecisionprob = 0.5
self.turned = False
self.turniter = 0
self.TURN_MAX = 50
self.OCCUPIED = 1
self.UNOCCUPIED = 0
def __init__(self):
self.lh = Lighthouse(.25, .75)
x = np.linspace(0, 1, 99)
y = np.linspace(0, 1, 99)
pdf = {(xi, yi): 1 for xi in x for yi in y}
likelihood = lambda x, (d, m): d / (d**2 + (x - m)**2)
self.bayes = Bayes(pdf, likelihood)
def __init__(self):
if os.path.getsize("bd_path.txt") > 0:
with open("bd_path.txt", 'rb') as f:
self._bd_path = f.readline()
f.close()
self._genre_features = np.load('genres_features.npy')
self._knn = KNN(self._bd_path, self._genre_features)
self._bayes = Bayes(self._bd_path, self._genre_features)
self._genres = np.load('genres.npy')
def change_db(self, path):
self._bd_path = path
with open("bd_path.txt", 'w') as f:
f.write(path)
f.close()
# self._genres, self._genre_features = extract_funtions.extrakt_features_for_genres(self._bd_path)
# np.save('genres.npy', self._genres)
# np.save("genres_features.npy", self._genre_features)
self._knn = KNN(self._bd_path, self._genre_features)
self._bayes = Bayes(self._bd_path, self._genre_features)
#self._knn.train_model()
self._bayes.train_model()
class Finder(object):
def __init__(self):
self.lh = Lighthouse(.25, .75)
x = np.linspace(0, 1, 99)
y = np.linspace(0, 1, 99)
pdf = {(xi, yi): 1 for xi in x for yi in y}
likelihood = lambda x, (d, m): d / (d**2 + (x - m)**2)
self.bayes = Bayes(pdf, likelihood)
def solve_stuff(self):
for datum in self.lh.dataset:
self.bayes.update(datum)
def print_distribution(self):
self.bayes.print_distribution()
def __init__(self):
self.lh = Lighthouse(.25,.75)
x = np.linspace(0, 1, 99)
y = np.linspace(0, 1, 99)
pdf = {(xi,yi):1 for xi in x for yi in y}
likelihood = lambda x, (d, m): d / (d**2 + (x-m)**2 )
self.bayes = Bayes(pdf, likelihood)
class Finder(object):
def __init__(self):
self.lh = Lighthouse(.25,.75)
x = np.linspace(0, 1, 99)
y = np.linspace(0, 1, 99)
pdf = {(xi,yi):1 for xi in x for yi in y}
likelihood = lambda x, (d, m): d / (d**2 + (x-m)**2 )
self.bayes = Bayes(pdf, likelihood)
def solve_stuff(self):
for datum in self.lh.dataset:
self.bayes.update(datum)
def print_distribution(self):
self.bayes.print_distribution()
def __init__(self, bzrc): self.bzrc = bzrc self.constants = self.bzrc.get_constants() self.commands = [] self.ALPHA = 0.01 self.BETA = 0.3 self.OBS_TOLERANCE = 35.0 self.S = 50 self.wroteonce = False self.goalradius = 30 self.tankradius = 5 self.avoidradius = 50 self.avoidBETA = 0.05 self.aimtolerance = math.pi/20 self.world_grid = WorldGrid() self.bayes = Bayes() self.turnprob = 0.05 self.turndecisionprob = 0.5 self.turned = False self.turniter = 0 self.TURN_MAX = 50 self.OCCUPIED = 1; self.UNOCCUPIED = 0;
def prep_model(can_load_model, load_path):
bayes = None
if can_load_model:
with open(load_path, "rb") as f:
bayes = pickle.load(f)
else:
bayes = Bayes()
return bayes
class Classifier:
def __init__(self):
if os.path.getsize("bd_path.txt") > 0:
with open("bd_path.txt", 'rb') as f:
self._bd_path = f.readline()
f.close()
self._genre_features = np.load('genres_features.npy')
self._knn = KNN(self._bd_path, self._genre_features)
self._bayes = Bayes(self._bd_path, self._genre_features)
self._genres = np.load('genres.npy')
def change_db(self, path):
self._bd_path = path
with open("bd_path.txt", 'w') as f:
f.write(path)
f.close()
# self._genres, self._genre_features = extract_funtions.extrakt_features_for_genres(self._bd_path)
# np.save('genres.npy', self._genres)
# np.save("genres_features.npy", self._genre_features)
self._knn = KNN(self._bd_path, self._genre_features)
self._bayes = Bayes(self._bd_path, self._genre_features)
#self._knn.train_model()
self._bayes.train_model()
def interpret_approx_array(self, track_path, model: Model):
genres = []
probabilities = []
probs = model.approx_genres(track_path)
probssort = np.argsort(probs)
probssort = np.flip(probssort, 0)
for i in probssort:
genres.append(self._genres[i])
probabilities.append(round(probs[i] * 100, 2))
return genres, probabilities # od największych do najmniejszych
def approximate(self, track_path, mode) -> (list, list):
if mode == "knn":
return self.interpret_approx_array(track_path, self._knn)
if mode == "bayes":
return self.interpret_approx_array(track_path, self._bayes)
return list(), list()
def get_db_name(self):
return self._bd_path
def main(): # Process CLI arguments. try: execname, host, port = sys.argv except ValueError: execname = sys.argv[0] print >>sys.stderr, '%s: incorrect number of arguments' % execname print >>sys.stderr, 'usage: %s hostname port' % sys.argv[0] sys.exit(-1) # Connect. #bzrc = BZRC(host, int(port), debug=True) bzrc = BZRC(host, int(port)) bayes = Bayes() constants = bzrc.get_constants() bayes.self_not_obs_given_not_occ(float(constants['truenegative'])) bayes.set_obs_given_occ(float(constants['truepositive'])) agent = Agent(bzrc, bayes) prev_time = time.time() # Run the agent try: while True: time_diff = time.time() - prev_time agent.tick(time_diff) except KeyboardInterrupt: print "Exiting due to keyboard interrupt." bzrc.close()
def modelOutput(trainFile, testFile, modelType):
"""
output is:
(naive bayes) variable name | 'class'
(tan) variable name | name of its parents
# empty
followed by:
predict class | actual class | posterior probability (12 digits after decimal point)
# empty
followed by:
The number of the test-set examples that were correctly classified.
"""
attributes, labels, instances = data_provider(trainFile)
if modelType == 'n':
model = Bayes(attributes, labels, instances)
elif modelType == 't':
model = TAN(attributes, labels, instances)
else:
import sys
print >> sys.stderr, 'model type should be [n] or [t] !!!'
sys.exit()
attributes, labels, instances = data_provider(testFile)
# format output part1: attribute name | 'class'
model.printTree()
print
correctClassCnt = 0
for test in instances:
result = model.classify(test)
if result[0] == result[1]:
correctClassCnt += 1
# format output part2: predict class | actual class | posterior probability
print formatOutput(result)
print
# format output part3: correctly classified number of test instances
print correctClassCnt
class Sentiment:
def __init__(self):
self.classifier = Bayes()
self.seg = Seg()
self.seg.load('seg.pickle')
def save(self, fname):
self.classifier.save(fname)
def load(self, fname):
self.classifier = self.classifier.load(fname)
def handle(self, doc):
words = self.seg.seg(doc)
words = self.filter_stop(words)
return words
def train(self, neg_docs, pos_docs):
datas = []
for doc in neg_docs:
datas.append([self.handle(doc), 'neg'])
for doc in pos_docs:
datas.append([self.handle(doc), 'pos'])
self.classifier.train(datas)
def classify(self, doc):
ret, prob = self.classifier.classify(self.handle(doc))
if ret == 'pos':
return prob
else:
return 1 - prob
@staticmethod
def filter_stop(words):
return list(filter(lambda x: x not in stop_words, words))
finalOutput.write(method + "," + line + "\n")
method = "SuperGrep"
print method
shutil.rmtree("cleanData/", True)
grep = Grep(True)
grep.initalPreprocess(DR, DT, L, TEST)
grep.testDirToOutput("cleanData/TEST/", "cleanData/" )
with open("cleanData/output.txt") as methodOutput:
for line in methodOutput.read().split():
finalOutput.write(method + "," + line + "\n")
method = "MultivariantBayes"
print method
shutil.rmtree("cleanData/", True)
bayes = Bayes('multivariant')
bayes.initalPreprocess(DR, DT, L, TEST, "custom", 1, False)
validate = CrossValidate("cleanData")
featureSelector = MIFeatureSelector()
validate.regularBayes(bayes, featureSelector, DISPLAY_ACCURACY)
with open("cleanData/output.txt") as methodOutput:
for line in methodOutput.read().split():
finalOutput.write(method + "," + line + "\n")
method = "MultinomialBayes"
print method
shutil.rmtree("cleanData/", True)
bayes = Bayes('multinomial')
bayes.initalPreprocess(DR, DT, L, TEST, "custom", 1, False)
validate = CrossValidate("cleanData")
featureSelector = FeatureSelector() # Multinomial does much worse with MIFeatureSelector()
class Agent(object): """Class handles all command and control logic for a teams tanks.""" def __init__(self, bzrc): self.bzrc = bzrc self.constants = self.bzrc.get_constants() self.commands = [] self.ALPHA = 0.01 self.BETA = 0.3 self.OBS_TOLERANCE = 35.0 self.S = 50 self.wroteonce = False self.goalradius = 30 self.tankradius = 5 self.avoidradius = 50 self.avoidBETA = 0.05 self.aimtolerance = math.pi/20 self.world_grid = WorldGrid() self.bayes = Bayes() self.turnprob = 0.05 self.turndecisionprob = 0.5 self.turned = False self.turniter = 0 self.TURN_MAX = 50 self.OCCUPIED = 1; self.UNOCCUPIED = 0; def tick(self, time_diff): """Some time has passed; decide what to do next.""" mytanks, othertanks, flags, shots = self.bzrc.get_lots_o_stuff() self.mytanks = mytanks self.othertanks = othertanks self.flags = [flag for flag in flags if flag.color != self.constants['team']] self.shots = shots self.enemies = [tank for tank in othertanks if tank.color != self.constants['team']] #self.obstacles = self.bzrc.get_obstacles() self.commands = [] for tank in mytanks: pos, grid = self.bzrc.get_occgrid(tank.index) self.update_priors(pos, grid) self.world_grid.draw_obstacle_grid() for tank in mytanks: self.goto_closest_target(tank) results = self.bzrc.do_commands(self.commands) def update_priors(self, position, grid): for x in range(len(grid)): for y in range(len(grid[x])): prior = self.world_grid.get_world_value(x, y, position) observation = grid[x][y] if observation == self.OCCUPIED: new_prior = self.bayes.probability_occupied_given_observed(prior) else: new_prior = self.bayes.probability_occupied_given_not_observed(prior) self.world_grid.set_world_value(x, y, position, new_prior) def move(self, mytanks): # it's not turning, so decide to turn or go straight if self.turned == False and self.turniter == 0: rand = random.random() # returns a number [0, 1) if rand < self.turnprob: for tank in mytanks: self.turn(tank) self.turned = True else: for tank in mytanks: self.go_straight(tank) else: # it is turning self.turniter = self.turniter + 1 if self.turniter >= self.TURN_MAX: self.turned = False self.turniter = 0 def turn(self, tank): rand = random.random() # returns a number [0, 1) if rand < self.turndecisionprob: command = Command(tank.index, 1, 1, False) # turn right self.commands.append(command) else: command = Command(tank.index, 0.5, -1, False) # turn left self.commands.append(command) def go_straight(self, tank): command = Command(tank.index, 1, 0, False) self.commands.append(command) def get_my_base(self): mybases = [base for base in self.bzrc.get_bases() if base.color == self.constants['team']] mybase = mybases[0] return mybase def get_base_center(self, base): center_x = ((base.corner1_x + base.corner2_x + base.corner3_x + base.corner4_x) / 4) center_y = ((base.corner1_y + base.corner2_y + base.corner3_y + base.corner4_y) / 4) return center_x, center_y def goto_closest_target(self, tank): best_tar = self.get_best_target(tank.x, tank.y) if best_tar is None: command = Command(tank.index, 0, 0, False) self.commands.append(command) else: print "Tank :", tank.x, tank.y print "Target: ", best_tar self.move_to_position(tank, best_tar[0], best_tar[1]) def get_best_target(self, x, y): best_tar = None best_dist = 2 * float(self.constants['worldsize']) for tar in self.world_grid.getTargetLocations(): dist = math.sqrt((tar[0] - x)**2 + (tar[1] - y)**2) if dist < best_dist: best_dist = dist best_tar = tar return best_tar def goto_flags(self, tank): best_flag = self.get_best_flag(tank.x, tank.y) if best_flag is None: command = Command(tank.index, 0, 0, False) self.commands.append(command) else: self.move_to_position(tank, best_flag.x, best_flag.y) def get_best_flag(self, x, y): best_flag = None best_dist = 2 * float(self.constants['worldsize']) for flag in self.flags: dist = math.sqrt((flag.x - x)**2 + (flag.y - y)**2) if dist < best_dist: best_dist = dist best_flag = flag return best_flag def avoid_target(self, my_x, my_y, target_x, target_y): goal_dist = math.sqrt((target_x - my_x)**2 + (target_y - my_y)**2) target_angle = math.atan2(target_y - my_y, target_x - my_x) dx = 0 dy = 0 s = self.avoidradius r = self.tankradius if goal_dist < self.tankradius: dx = -1 * math.cos(target_angle) * 1000 #infinity dy = -1 * math.sin(target_angle) * 1000 #infinity elif goal_dist >= self.tankradius and goal_dist <= (s + r): dx = -1 * self.avoidBETA * (s + r - goal_dist) * math.cos(target_angle) dy = -1 * self.avoidBETA * (s + r - goal_dist) * math.sin(target_angle) else: dx = 0 dy = 0 return dx, dy def calculate_enemies_delta(self, my_x, my_y, enemies): delta_x = 0 delta_y = 0 for tank in enemies: dx, dy = self.avoid_target(my_x, my_y, tank.x, tank.y) delta_x += dx delta_y += dy sqnorm = math.sqrt(delta_x**2 + delta_y**2) if sqnorm == 0: delta_x = 0 delta_y = 0 else: delta_x = delta_x / sqnorm delta_y = delta_y / sqnorm return delta_x, delta_y def calculate_objective_delta(self, my_x, my_y, target_x, target_y): goal_dist = math.sqrt((target_x - my_x)**2 + (target_y - my_y)**2) target_angle = math.atan2(target_y - my_y, target_x - my_x) delta_xG = self.ALPHA * goal_dist * math.cos(target_angle) # r = 0 delta_yG = self.ALPHA * goal_dist * math.sin(target_angle) # r = 0 sqnorm = math.sqrt(delta_xG**2 + delta_yG**2) #set magnitude magnitude = 0 if goal_dist > self.goalradius: magnitude = 1 else: magnitude = sqnorm if sqnorm == 0: delta_xG = 0 delta_yG = 0 else: delta_xG = delta_xG / sqnorm delta_yG = delta_yG / sqnorm return delta_xG, delta_yG, magnitude """def calculate_obstacles_delta(self, x, y): delta_xO = 0 delta_yO = 0 for obs in self.obstacles: repel_xO, repel_yO = self.get_obstacle_force(obs, x, y) delta_xO += repel_xO delta_yO += repel_yO sqnorm = math.sqrt(delta_xO**2 + delta_yO**2) if sqnorm == 0: delta_xO = 0 delta_yO = 0 else: delta_xO = delta_xO / sqnorm delta_yO = delta_yO / sqnorm '''if delta_xG != 0 or delta_yG != 0: print "delta_xO: ", delta_xO print "delta_yO: ", delta_yO''' return delta_xO, delta_yO""" def calculate_random_delta(self): dx = random.uniform(-.01, .01) dy = random.uniform(-.01, .01) return dx, dy def should_fire(self, tank): for enemy in self.enemies: target_angle = math.atan2(enemy.y - tank.y, enemy.x - tank.x) if abs(tank.angle - target_angle) < self.aimtolerance: return True return False def move_to_position(self, tank, target_x, target_y): """Set command to move to given coordinates.""" #get deltas delta_xG, delta_yG, magnitude = self.calculate_objective_delta(tank.x, tank.y, target_x, target_y) #delta_xO, delta_yO = self.calculate_obstacles_delta(tank.x, tank.y) delta_xO, delta_yO = 0, 0 delta_xR, delta_yR = self.calculate_random_delta() #delta_xA, delta_yA = self.calculate_enemies_delta(tank.x, tank.y, self.enemies) #combine delta_x = delta_xG + delta_xO + delta_xR #+ delta_xA delta_y = delta_yG + delta_yO + delta_yR #+ delta_xA #calculate angle turn_angle = math.atan2(delta_y, delta_x) relative_angle = self.normalize_angle(turn_angle - tank.angle) #put lower bound on speed: no slower than 40% if magnitude < 0.4: magnitude = 0.4 fire = self.should_fire(tank) command = Command(tank.index, magnitude, 2 * relative_angle, False) self.commands.append(command) def get_obstacle_force(self, obstacle, x, y): delta_x = 0 delta_y = 0 big_num = 5000 min_x = big_num max_x = -big_num min_y = big_num max_y = -big_num for p in obstacle: repel_x, repel_y = self.get_tangential_field(x, y, p) delta_x += repel_x delta_y += repel_y xp, yp = p if xp < min_x: min_x = xp elif xp > max_x: max_x = xp if yp < min_y: min_y = yp elif yp > max_y: max_y = yp center_point = [(min_x + min_y) / 2, (min_y + max_y) / 2] repel_x, repel_y = self.get_repel_field(x, y, center_point[0], center_point[1]) # repel from the center of the box delta_x += repel_x delta_y += repel_y return delta_x, delta_y def get_tangential_field(self, x, y, p): my_x = x my_y = y xO, yO = p dist = math.sqrt( (my_x - xO)**2 + (my_y - yO)**2 ) if dist < self.OBS_TOLERANCE: # angle between the tank and the obstacle theta = math.atan2(yO - y, xO - x) + math.pi / 2 delta_x = -self.BETA * (self.avoidradius - dist) * math.cos(theta) delta_y = -self.BETA * (self.avoidradius - dist) * math.sin(theta) return delta_x, delta_y else: return 0, 0 def get_repel_field(self, x, y, xO, yO): my_x = x my_y = y dist = math.sqrt( (my_x - xO)**2 + (my_y - yO)**2 ) if dist < self.OBS_TOLERANCE: # angle between the tank and the obstacle theta = math.atan2(yO - y, xO - x) delta_x = -self.BETA * (self.avoidradius - dist) * math.cos(theta) # math.cos returns in radians delta_y = -self.BETA * (self.avoidradius - dist) * math.sin(theta) return delta_x, delta_y else: return 0, 0 def normalize_angle(self, angle): """Make any angle be between +/- pi.""" angle -= 2 * math.pi * int (angle / (2 * math.pi)) if angle <= -math.pi: angle += 2 * math.pi elif angle > math.pi: angle -= 2 * math.pi return angle
print("_" * 15 + "BAYES CLASSIFIER" + "_" * 15)
print_menu()
classifier = None
while (True):
command = input("Enter command:")
command = command.lower()
#Train clause
if command.startswith('t'):
classifier = pp.main()
#Load training clause
elif command.startswith('l'):
print("Loading: ", end='')
if classifier is None:
classifier = Bayes(trained=True)
else:
classifier.load()
#Save training clause
elif command.startswith('s'):
print("Saving: ", end='')
if classifier is not None:
classifier.save()
else:
print("Nothing to save")
#Classify clause
elif command.startswith('c'):
if classifier is None:
print("Load training first")
else:
path = command.split(" ")
'''数据加载'''
data = pda.read_csv("./iris.csv")
'''标准化'''
data_standard = preprocessing.scale(data.iloc[:, :-1])
'''切分数据集，处理过拟合'''
train_data, test_data, train_labels, test_labels = train_test_split(
data_standard,
data.as_matrix()[:, -1],
test_size=0.2,
random_state=int(time.time()))
'''
贝叶斯算法识别
'''
print("---------------------------贝叶斯----------------------------------")
start = time.clock()
by = Bayes()
by.train(list(train_data), list(train_labels))
test_data_size = test_data.shape[0]
error_count = 0
for index, td in enumerate(list(test_data)):
this_label = by.test(td)
print("预测类别：{0}，真实类别：{1}".format(this_label, test_labels[index]))
if this_label != test_labels[index]:
error_count += 1
end = time.clock()
error_rate = (error_count / test_data_size) * 100
time_consuming = end - start
print("错误率为：{0:.2f}%".format(error_rate))
print("耗时：{0:.4f}s".format(time_consuming))
'''
k-近邻算法识别
def __init__(self):
self.classifier = Bayes()
self.seg = Seg()
self.seg.load('seg.pickle')
class Agent(object):
"""Class handles all command and control logic for a teams tanks."""
def __init__(self, bzrc):
self.bzrc = bzrc
self.constants = self.bzrc.get_constants()
self.commands = []
self.ALPHA = 0.01
self.BETA = 0.3
self.OBS_TOLERANCE = 35.0
self.S = 50
self.wroteonce = False
self.goalradius = 30
self.tankradius = 5
self.avoidradius = 50
self.avoidBETA = 0.05
self.aimtolerance = math.pi / 20
self.world_grid = WorldGrid()
self.bayes = Bayes()
self.turnprob = 0.05
self.turndecisionprob = 0.5
self.turned = False
self.turniter = 0
self.TURN_MAX = 50
self.OCCUPIED = 1
self.UNOCCUPIED = 0
def tick(self, time_diff):
"""Some time has passed; decide what to do next."""
mytanks, othertanks, flags, shots = self.bzrc.get_lots_o_stuff()
self.mytanks = mytanks
self.othertanks = othertanks
self.flags = [
flag for flag in flags if flag.color != self.constants['team']
]
self.shots = shots
self.enemies = [
tank for tank in othertanks if tank.color != self.constants['team']
]
#self.obstacles = self.bzrc.get_obstacles()
self.commands = []
for tank in mytanks:
pos, grid = self.bzrc.get_occgrid(tank.index)
self.update_priors(pos, grid)
self.world_grid.draw_obstacle_grid()
for tank in mytanks:
self.goto_closest_target(tank)
results = self.bzrc.do_commands(self.commands)
def update_priors(self, position, grid):
for x in range(len(grid)):
for y in range(len(grid[x])):
prior = self.world_grid.get_world_value(x, y, position)
observation = grid[x][y]
if observation == self.OCCUPIED:
new_prior = self.bayes.probability_occupied_given_observed(
prior)
else:
new_prior = self.bayes.probability_occupied_given_not_observed(
prior)
self.world_grid.set_world_value(x, y, position, new_prior)
def move(self, mytanks):
# it's not turning, so decide to turn or go straight
if self.turned == False and self.turniter == 0:
rand = random.random() # returns a number [0, 1)
if rand < self.turnprob:
for tank in mytanks:
self.turn(tank)
self.turned = True
else:
for tank in mytanks:
self.go_straight(tank)
else: # it is turning
self.turniter = self.turniter + 1
if self.turniter >= self.TURN_MAX:
self.turned = False
self.turniter = 0
def turn(self, tank):
rand = random.random() # returns a number [0, 1)
if rand < self.turndecisionprob:
command = Command(tank.index, 1, 1, False) # turn right
self.commands.append(command)
else:
command = Command(tank.index, 0.5, -1, False) # turn left
self.commands.append(command)
def go_straight(self, tank):
command = Command(tank.index, 1, 0, False)
self.commands.append(command)
def get_my_base(self):
mybases = [
base for base in self.bzrc.get_bases()
if base.color == self.constants['team']
]
mybase = mybases[0]
return mybase
def get_base_center(self, base):
center_x = ((base.corner1_x + base.corner2_x + base.corner3_x +
base.corner4_x) / 4)
center_y = ((base.corner1_y + base.corner2_y + base.corner3_y +
base.corner4_y) / 4)
return center_x, center_y
def goto_closest_target(self, tank):
best_tar = self.get_best_target(tank.x, tank.y)
if best_tar is None:
command = Command(tank.index, 0, 0, False)
self.commands.append(command)
else:
print "Tank :", tank.x, tank.y
print "Target: ", best_tar
self.move_to_position(tank, best_tar[0], best_tar[1])
def get_best_target(self, x, y):
best_tar = None
best_dist = 2 * float(self.constants['worldsize'])
for tar in self.world_grid.getTargetLocations():
dist = math.sqrt((tar[0] - x)**2 + (tar[1] - y)**2)
if dist < best_dist:
best_dist = dist
best_tar = tar
return best_tar
def goto_flags(self, tank):
best_flag = self.get_best_flag(tank.x, tank.y)
if best_flag is None:
command = Command(tank.index, 0, 0, False)
self.commands.append(command)
else:
self.move_to_position(tank, best_flag.x, best_flag.y)
def get_best_flag(self, x, y):
best_flag = None
best_dist = 2 * float(self.constants['worldsize'])
for flag in self.flags:
dist = math.sqrt((flag.x - x)**2 + (flag.y - y)**2)
if dist < best_dist:
best_dist = dist
best_flag = flag
return best_flag
def avoid_target(self, my_x, my_y, target_x, target_y):
goal_dist = math.sqrt((target_x - my_x)**2 + (target_y - my_y)**2)
target_angle = math.atan2(target_y - my_y, target_x - my_x)
dx = 0
dy = 0
s = self.avoidradius
r = self.tankradius
if goal_dist < self.tankradius:
dx = -1 * math.cos(target_angle) * 1000 #infinity
dy = -1 * math.sin(target_angle) * 1000 #infinity
elif goal_dist >= self.tankradius and goal_dist <= (s + r):
dx = -1 * self.avoidBETA * (s + r -
goal_dist) * math.cos(target_angle)
dy = -1 * self.avoidBETA * (s + r -
goal_dist) * math.sin(target_angle)
else:
dx = 0
dy = 0
return dx, dy
def calculate_enemies_delta(self, my_x, my_y, enemies):
delta_x = 0
delta_y = 0
for tank in enemies:
dx, dy = self.avoid_target(my_x, my_y, tank.x, tank.y)
delta_x += dx
delta_y += dy
sqnorm = math.sqrt(delta_x**2 + delta_y**2)
if sqnorm == 0:
delta_x = 0
delta_y = 0
else:
delta_x = delta_x / sqnorm
delta_y = delta_y / sqnorm
return delta_x, delta_y
def calculate_objective_delta(self, my_x, my_y, target_x, target_y):
goal_dist = math.sqrt((target_x - my_x)**2 + (target_y - my_y)**2)
target_angle = math.atan2(target_y - my_y, target_x - my_x)
delta_xG = self.ALPHA * goal_dist * math.cos(target_angle) # r = 0
delta_yG = self.ALPHA * goal_dist * math.sin(target_angle) # r = 0
sqnorm = math.sqrt(delta_xG**2 + delta_yG**2)
#set magnitude
magnitude = 0
if goal_dist > self.goalradius:
magnitude = 1
else:
magnitude = sqnorm
if sqnorm == 0:
delta_xG = 0
delta_yG = 0
else:
delta_xG = delta_xG / sqnorm
delta_yG = delta_yG / sqnorm
return delta_xG, delta_yG, magnitude
"""def calculate_obstacles_delta(self, x, y):
delta_xO = 0
delta_yO = 0
for obs in self.obstacles:
repel_xO, repel_yO = self.get_obstacle_force(obs, x, y)
delta_xO += repel_xO
delta_yO += repel_yO
sqnorm = math.sqrt(delta_xO**2 + delta_yO**2)
if sqnorm == 0:
delta_xO = 0
delta_yO = 0
else:
delta_xO = delta_xO / sqnorm
delta_yO = delta_yO / sqnorm
'''if delta_xG != 0 or delta_yG != 0:
print "delta_xO: ", delta_xO
print "delta_yO: ", delta_yO'''
return delta_xO, delta_yO"""
def calculate_random_delta(self):
dx = random.uniform(-.01, .01)
dy = random.uniform(-.01, .01)
return dx, dy
def should_fire(self, tank):
for enemy in self.enemies:
target_angle = math.atan2(enemy.y - tank.y, enemy.x - tank.x)
if abs(tank.angle - target_angle) < self.aimtolerance:
return True
return False
def move_to_position(self, tank, target_x, target_y):
"""Set command to move to given coordinates."""
#get deltas
delta_xG, delta_yG, magnitude = self.calculate_objective_delta(
tank.x, tank.y, target_x, target_y)
#delta_xO, delta_yO = self.calculate_obstacles_delta(tank.x, tank.y)
delta_xO, delta_yO = 0, 0
delta_xR, delta_yR = self.calculate_random_delta()
#delta_xA, delta_yA = self.calculate_enemies_delta(tank.x, tank.y, self.enemies)
#combine
delta_x = delta_xG + delta_xO + delta_xR #+ delta_xA
delta_y = delta_yG + delta_yO + delta_yR #+ delta_xA
#calculate angle
turn_angle = math.atan2(delta_y, delta_x)
relative_angle = self.normalize_angle(turn_angle - tank.angle)
#put lower bound on speed: no slower than 40%
if magnitude < 0.4:
magnitude = 0.4
fire = self.should_fire(tank)
command = Command(tank.index, magnitude, 2 * relative_angle, False)
self.commands.append(command)
def get_obstacle_force(self, obstacle, x, y):
delta_x = 0
delta_y = 0
big_num = 5000
min_x = big_num
max_x = -big_num
min_y = big_num
max_y = -big_num
for p in obstacle:
repel_x, repel_y = self.get_tangential_field(x, y, p)
delta_x += repel_x
delta_y += repel_y
xp, yp = p
if xp < min_x:
min_x = xp
elif xp > max_x:
max_x = xp
if yp < min_y:
min_y = yp
elif yp > max_y:
max_y = yp
center_point = [(min_x + min_y) / 2, (min_y + max_y) / 2]
repel_x, repel_y = self.get_repel_field(
x, y, center_point[0],
center_point[1]) # repel from the center of the box
delta_x += repel_x
delta_y += repel_y
return delta_x, delta_y
def get_tangential_field(self, x, y, p):
my_x = x
my_y = y
xO, yO = p
dist = math.sqrt((my_x - xO)**2 + (my_y - yO)**2)
if dist < self.OBS_TOLERANCE:
# angle between the tank and the obstacle
theta = math.atan2(yO - y, xO - x) + math.pi / 2
delta_x = -self.BETA * (self.avoidradius - dist) * math.cos(theta)
delta_y = -self.BETA * (self.avoidradius - dist) * math.sin(theta)
return delta_x, delta_y
else:
return 0, 0
def get_repel_field(self, x, y, xO, yO):
my_x = x
my_y = y
dist = math.sqrt((my_x - xO)**2 + (my_y - yO)**2)
if dist < self.OBS_TOLERANCE:
# angle between the tank and the obstacle
theta = math.atan2(yO - y, xO - x)
delta_x = -self.BETA * (self.avoidradius - dist) * math.cos(
theta) # math.cos returns in radians
delta_y = -self.BETA * (self.avoidradius - dist) * math.sin(theta)
return delta_x, delta_y
else:
return 0, 0
def normalize_angle(self, angle):
"""Make any angle be between +/- pi."""
angle -= 2 * math.pi * int(angle / (2 * math.pi))
if angle <= -math.pi:
angle += 2 * math.pi
elif angle > math.pi:
angle -= 2 * math.pi
return angle
def __init__(self, attributes, labels, traindata):
Bayes.__init__(self, attributes, labels, traindata)
self.edges = self.initializeTree()
self.graph = self.findMaxSpanningTree()
config = json.load(f)
# update the configuration for the new version of the data
config["csv"] = "test/weather_v2.csv"
config["inputs"] = data.drop(columns=["city", "date", "avg_temp"]).columns.tolist()
# test features
# config["inputs"] = None
# config["resolution"] = None
config["input_history"] = False
# In[2]: Model the data
# produce a bayesian ridge regression rolling forecast
print("---- Bayesian Ridge Regression ----")
model5 = Bayes(**config)
model5.roll(verbose=True)
print(f"Bayesian Average Error: {np.round(model5._error.mean()[0] * 100, 2)}%")
print("---- PLS ----")
model4 = PLS(**config)
model4.roll(verbose=True)
print(f"PLS Average Error: {np.round(model4._error.mean()[0] * 100, 2)}%")
# produce a neural network rolling forecast
print("---- Neural Network ----")
model3 = MLP(**config)
model3.roll(verbose=True)
print(f"NNet Average Error: {np.round(model3._error.mean()[0] * 100, 2)}%")
# produce a random forest rolling forecast
return p
elif val == 'T':
return 1-p
'''Make a graph with 8 subplots that has the posterior for each of the following scenarios.
Make sure to give each graph a title!
* You get the data: H
* You get the data: T
* You get the data: H, H
* You get the data: T, H
* You get the data: H, H, H
* You get the data: T, H, T
* You get the data: H, H, H, H
* You get the data: T, H, T, H'''
bayes_uniform = Bayes(prior_dict.copy(),likelihood_func=likelihood)
bayes_uniform.update('H')
fig, axs = plt.subplots(4,2, figsize=(14,8))
scenarios = ['H','T',['H','H'],['T','H'],['H','H','H'],['T','H','T'],['H','H','H','H'],['T','H','T','H']]
i = 1
for scenario, ax in zip(scenarios, axs.flatten()):
bayes = Bayes(prior_dict.copy(),likelihood_func=likelihood)
for flip in scenario:
bayes.update(flip)
bayes.plot(ax,title='Scenario #'+str(i)+': '+ ', '.join(scenario))
i+=1
plt.tight_layout()
plt.show()
'''On a single graph, Use the coin.py random coin generator and overlay the initial uniform prior with the prior after 1, 2, 10, 50 and 250 flips..
def main():
get_data('data/boy82.txt', 'boy')
get_data('data/boy83.txt', 'boy')
get_data('data/boynew.txt', 'boy')
get_data('data/girl35.txt', 'girl')
get_data('data/girl42.txt', 'girl')
get_data('data/girlnew.txt', 'girl')
test3 = []
testhw = []
tesths = []
testws = []
boys = open('data/boy.txt')
girls = open('data/girl.txt')
print("1) Bayes")
print("2) Fisher")
print("3) kNN")
choice = input("Input the algorithm: ")
if choice == '1':
for line in boys.readlines():
height, weight, shoe_size = line.split()
test3.append([[float(height)], [float(weight)], [float(shoe_size)], 1])
testhw.append([[float(height)], [float(weight)], 1])
tesths.append([[float(height)], [float(shoe_size)], 1])
testws.append([[float(weight)], [float(shoe_size)], 1])
for line in girls.readlines():
height, weight, shoe_size = line.split()
test3.append([[float(height)], [float(weight)], [float(shoe_size)], 0])
testhw.append([[float(height)], [float(weight)], 0])
tesths.append([[float(height)], [float(shoe_size)], 0])
testws.append([[float(weight)], [float(shoe_size)], 0])
plt.xlim(0, 1.0)
plt.ylim(0, 1.0)
plt.plot([0, 1.0], [0, 1.0], color='red')
b3 = Bayes(trains3)
b3.paint(test3, 'b')
bhw = Bayes(trainshw)
bhw.paint(testhw, 'g')
bhs = Bayes(trainshs)
bhs.paint(tesths, 'r')
bws = Bayes(trainsws)
bws.paint(testws, 'y')
elif choice == '2':
for line in boys.readlines():
height, weight, shoe_size = line.split()
test3.append([[float(height)], [float(weight)], [float(shoe_size)], 1])
testhw.append([[float(height)], [float(weight)], 1])
tesths.append([[float(height)], [float(shoe_size)], 1])
testws.append([[float(weight)], [float(shoe_size)], 1])
for line in girls.readlines():
height, weight, shoe_size = line.split()
test3.append([[float(height)], [float(weight)], [float(shoe_size)], 0])
testhw.append([[float(height)], [float(weight)], 0])
tesths.append([[float(height)], [float(shoe_size)], 0])
testws.append([[float(weight)], [float(shoe_size)], 0])
f3 = Fisher(trains3)
fhw = Fisher(trainshw)
fhs = Fisher(trainshs)
fws = Fisher(trainsws)
print("1) ROC")
print("2) Line")
choice = input("Choose: ")
if choice == '1':
f3.paint(test3, 'b')
fhw.paint(testhw, 'g')
fhs.paint(tesths, 'r')
fws.paint(testws, 'y')
elif choice == '2':
print("1) Height And Weight")
print("2) Height And Shoe Size")
print("3) Weight And Shoe Size")
choice = input("Choose: ")
if choice == '1':
fhw.paint_line(testhw, 'c')
elif choice == '2':
fhs.paint_line(tesths, 'r')
elif choice == '3':
fws.paint_line(testws, 'y')
elif choice == '3':
for line in boys.readlines():
height, weight, shoe_size = line.split()
test3.append([float(height), float(weight), float(shoe_size), 1])
testhw.append([float(height), float(weight), 1])
tesths.append([float(height), float(shoe_size), 1])
testws.append([float(weight), float(shoe_size), 1])
for line in girls.readlines():
height, weight, shoe_size = line.split()
test3.append([float(height), float(weight), float(shoe_size), 0])
testhw.append([float(height), float(weight), 0])
tesths.append([float(height), float(shoe_size), 0])
testws.append([float(weight), float(shoe_size), 0])
choice = input("Input K:")
if choice == '1':
k1 = kNN(trainshs, 1)
print(k1.test(tesths))
k1.paint()
elif choice == '3':
k3 = kNN(trainshs, 3)
print(k3.test(tesths))
k3.paint()
elif choice == '5':
k5 = kNN(trainshs, 5)
print(k5.test(tesths))
k5.paint()
plt.show()
def load_model(self, model_path):
self.model = Bayes.load(model_path)
die (int): number of sides of the die that produced the roll
Returns:
likelihood (float): the probability of the roll given the die.
"""
if roll in range(1, die + 1):
return 1 / die
else:
return 0
if __name__ == '__main__':
uniform_prior = {4: .08, 6: .12, 8: .16, 12: .24, 20: .40}
unbalanced_prior = {}
die_bayes_1 = Bayes(uniform_prior.copy(), die_likelihood)
experiment = [
8, 2, 1, 2, 5, 8, 2, 4, 3, 7, 6, 5, 1, 6, 2, 5, 8, 8, 5, 3, 4, 2, 4, 3,
8, 8, 7, 8, 8, 8, 5, 5, 1, 3, 8, 7, 8, 5, 2, 5, 1, 4, 1, 2, 1, 3, 1, 3,
1, 5
]
experiment2 = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
experiment3 = [20, 20, 20, 20, 20, 20, 20, 20, 20, 20]
for idx, roll in enumerate(experiment3):
print('roll#={} dic rolled={}'.format(idx + 1, roll))
die_bayes_1.update(roll)
die_bayes_1.print_distribution()
#unbalanced_prior = die_bayes_1.prior
print('*' * 32)
import pandas
from matplotlib import pyplot
from bayes import Bayes
from utils import generate_datasets
data = pandas.read_csv('./datasets/leaf.csv')
labels = data["species"]
data.drop(data.columns[-1], axis=1, inplace=True)
print(data.index)
for dataset in generate_datasets(data, labels):
print('\n' + dataset.name)
for training_percent in range(60, 91, 5):
classifier = Bayes(dataset.data, labels, training_percent)
classifier.train()
classifier.test()
dataset.result.append(classifier.get_accuracy())
print('Training percent: ' + str(training_percent) + '%, accuracy: ' +
str(classifier.get_accuracy()))
pyplot.plot(range(60, 91, 5), dataset.result, label=dataset.name)
pyplot.xlabel('Training percent')
pyplot.ylabel('Accuracy')
pyplot.legend()
pyplot.savefig('plot', dpi=200, bbox_inches='tight')
model_now += 1
print("SVM 1训练中...")
svm = Svm(svm_label, svm_images, svm_test_label, svm_test_images)
results[model_now] = svm.train(numToClassfy, numToTrain, "-q -m 1000")
model_now += 1
print("SVM 2训练中...")
svm = Svm(svm_label, svm_images, svm_test_label, svm_test_images)
results[model_now] = svm.train(numToClassfy, numToTrain, "-q -m 1000 -t 3")
model_now += 1
knn = knn(10, images, label)
results[model_now] = knn.start(test_images, test_label, numToClassfy)
model_now += 1
bayes = Bayes(10, 784, images, label)
results[model_now] = bayes.start(test_images, test_label, numToClassfy)
model_now += 1
results = results.T
count = 0
for i in range(numToClassfy):
progress = Progress(numToClassfy, "正在投票")
k = np.argmax(np.bincount(results[i]))
if (k == test_label[i]):
count += 1
progress.updata(i + 1)
print("集成识别完成，样本个数: " + str(numToClassfy) + " 识别数: " + str(count) +
" 正确率: %.2f %%" % ((count / numToClassfy) * 100))
def train_model(ngrams_file, output_file):
model = Bayes()
model.train(Ngrams._load_data(ngrams_file))
model.serialize(output_file)
from bayes import Bayes
# First you need to create an instance of this
# algorithm and defined what field/column
# you want to classify
instance = Bayes("Sex")
# Secondly, you will need to learn about a set
# of data to train the algorithm
instance.learn("static/data_test.csv")
# Finally you can use your trained instance to
# classify a set of data (In this example we
# will find the most probable sex)
print(instance.classify([6, 130, 8]))
return 0
uniform_prior = {4: 0.2, 6: 0.2, 8: 0.2, 12: 0.2, 20: 0.2}
unbalanced_prior = {4: 0.08, 6: 0.12, 8: 0.16, 12: 0.24, 20: 0.4}
d = [
8, 2, 1, 2, 5, 8, 2, 4, 3, 7, 6, 5, 1, 6, 2, 5, 8, 8, 5, 3, 4, 2, 4, 3, 8,
8, 7, 8, 8, 8, 5, 5, 1, 3, 8, 7, 8, 5, 2, 5, 1, 4, 1, 2, 1, 3, 1, 3, 1, 5
]
set1 = [1, 1, 1, 3, 1, 2]
set2 = [10, 10, 10, 10, 8, 8]
print('What are the posteriors if we started with the uniform prior?')
bayes_uniform = Bayes(uniform_prior.copy(), likelihood_func=likelihood_func)
bayes_uniform.update(8)
bayes_uniform.print_distribution()
print('What are the posteriors if we started with the unbalanced prior?')
bayes_unbalanced = Bayes(unbalanced_prior.copy(),
likelihood_func=likelihood_func)
bayes_unbalanced.update(8)
bayes_unbalanced.print_distribution()
print(
'How different were these two posteriors (the uniform from the unbalanced)?'
)
for k, v in bayes_unbalanced.posterior.items():
print("{} : {}".format(
k, bayes_uniform.posterior[k] - bayes_unbalanced.posterior[k]))
from bayes import Bayes, URGENCIES
from sys import argv
import json
from signal import signal, SIGPIPE, SIG_DFL
signal(SIGPIPE, SIG_DFL)
f = open(argv[1], 'r')
json_str = f.read()
message_list = json.loads(json_str)
bae = Bayes(message_list)
bae.train()
test_data = open(argv[2], 'r')
test_data = test_data.read()
test_data = json.loads(test_data)
def prob_class(string, clazz):
S = set(string.split())
fv = bae.gen_feature_vector(S)
return bae.prob_class(fv, clazz)
def main():
tests = test_data
out = {}
for test in tests:
o = {}
for u in URGENCIES.keys():
from estudiante import Atributo,EstudianteBuilder
from bayes import Bayes
from random import shuffle
from setup import Setup
Setup.setup()
atributos = Setup.atributos
estudiantes = Setup.estudiantes
valoresPosibles = Setup.valoresPosibles
# Se genera una instancia del algoritmo pasandole el atributo objetivo
bayes = Bayes("G3", atributos, valoresPosibles)
# ------------------
# VALIDACION DE BAYES
# ------------------
# Se realizaran multiples pruebas para obtener una estimacion de la efectividad del algoritmo
# para cualquier eleccion de casos de entrenamiento / prueba
CANTIDAD_PRUEBAS = 100
# Separo 1/5 de los casos de prueba
cantEstTest = len(estudiantes)/5
shuffle(estudiantes)
from bayes import Bayes
# calculate validation accuracy
def accuracy(type, result):
if type == 'ham':
num = len(result) - sum(result)
return float(num / len(result))
elif type == 'spam':
num = sum(result)
return float(num / len(result))
return 0
# now do the prediction, when you run the code change the file path to train_1, train_2, train_3, train_4, train_5, test_1....
ham_file = './ham/train_1.txt'
spam_file = './spam/train_1.txt'
test_file = './ham/test_1.txt'
train_matrix, class_labels, test_doc = Bayes.process_include_test_data(ham_file, spam_file, test_file)
test_file_2 = './spam/test_1.txt'
train_matrix_2, class_labels_2, test_doc_2 = Bayes.process_include_test_data(ham_file, spam_file, test_file_2)
result = Bayes.MyMultinomialNB(train_matrix, class_labels, test_doc)
result2 = Bayes.MyMultinomialNB(train_matrix_2, class_labels_2, test_doc_2)
print('accuracy of ham: ' + str(accuracy('ham', result)))
print('accuracy of spam: ' + str(accuracy('spam', result2)))
precision=len(test_doc_2)*accuracy('spam', result2)/(len(test_doc_2)*accuracy('spam', result2)+len(test_doc)*(1-accuracy('ham', result)))
recall = accuracy('spam', result2)
F_1=2*precision*recall/(precision+recall)
print('precision '+str(precision))
print('recall ' +str(recall))
print('F_1 '+str(F_1))
def main():
#Making list of .txt-files (per sentiment)
print("\tLOADING FILES")
path = Path('..').joinpath('Data')
test_ = path.joinpath('test')
train = path.joinpath('train')
tp_reviews = txtToList(test_.joinpath('pos'))
tn_reviews = txtToList(test_.joinpath("neg"))
pos_reviews = txtToList(train.joinpath("pos"))
neg_reviews = txtToList(train.joinpath("neg"))
print("\tFILES LOADED")
#Cleaning reviews
reviews = [pos_reviews, neg_reviews, tp_reviews, tn_reviews]
print("\tCLEANING REVIEWS")
for list_ in reviews:
for i, review in enumerate(list_):
list_[i] = clean_text(review)
#Joining the reviews into one string (per sentiment)
pos_string = "".join([string for string in pos_reviews])
neg_string = "".join([string for string in neg_reviews])
#Counting the frequency of words (per sentiment and total)
posCounter = Counter(pos_string.split())
negCounter = Counter(neg_string.split())
vocabCounter = Counter(pos_string.split() + neg_string.split())
for term in list(posCounter):
if (posCounter[term] == 1):
del posCounter[term]
for term in list(negCounter):
if (negCounter[term] == 1):
del negCounter[term]
classifier = Bayes(vocab_counts=vocabCounter)
classifier.train(posCounter, negCounter)
testSets = [tp_reviews, tn_reviews]
n_pos_tp, n_neg_tp = 0, 0
n_pos_tn, n_neg_tn = 0, 0
for i, testSet in enumerate(testSets):
print("_" * 15 + "RESULTS" + "_" * 15)
n_pos, n_neg = 0, 0
for review in testSet:
pos, neg = classifier.test(review)
if (pos >= neg):
n_pos += 1
else:
n_neg += 1
if (i == 0):
print("Positive Testset: ")
n_pos_tp, n_neg_tp = n_pos, n_neg
else:
print("Negative Testset: ")
n_pos_tn, n_neg_tn = n_pos, n_neg
print("Positive reviews: {}".format(n_pos))
print("Negative reviews: {}".format(n_neg))
pos_prec = n_pos_tp / (n_pos_tp + len(tn_reviews) - n_neg_tn)
pos_rec = n_pos_tp / len(tp_reviews)
pos_f1 = 2 * ((pos_prec * pos_rec) / (pos_prec + pos_rec))
neg_prec = n_neg_tn / (n_neg_tn + len(tp_reviews) - n_pos_tp)
neg_rec = n_neg_tn / len(tn_reviews)
neg_f1 = 2 * ((neg_prec * neg_rec) / (neg_prec + neg_rec))
scores = [pos_prec, pos_rec, pos_f1, neg_prec, neg_rec, neg_f1]
save_stats(scores)
print_stats(scores)
return classifier
