def train(self):
global fbest,wbest
f_list = []
for i in range(G):
for p in self.particles:
f = self.game(p.x)
f_list.append(f[1])
if f[1] > fbest:
fbest = f[1]
wbest = f[0]
if p.f < f[1]:
p.f = f[1]
p.pbest = copy.copy(p.x)
if self.f < p.f: self.update(p.x,p.f)
for p in self.particles: p.move(self.gbest)
print(f"{i+1}: {max(f_list)}")
#ネットワーク保存
if i == int(G/10) -1: self.save(wbest,i+1)
if i == int(G/2) -1: self.save(wbest,i+1)
print(fbest)
plot(f_list)
return wbest
def train(self):
global fbest, wbest
for i in range(G):
for w in self.w_list:
p = self.game(w)
if p[1] > fbest:
fbest = p[1]
wbest = p[0]
self.p_list.append(self.Parent(p[0], p[1]))
self.f_list.append(p[1])
#評価の良い順にソート
self.p_list = sorted(self.p_list, key=lambda p: p.f, reverse=True)
print(f"{i+1}: ", max(self.f_list))
#淘汰
del self.p_list[P:]
#新たな子の生成
self.w_list = [
self.p_list[random.randint(0, P - 1)].mutation()
for i in range(C)
]
if i == int(G / 10) - 1: self.save(wbest, i + 1)
if i == int(G / 2) - 1: self.save(wbest, i + 1)
print(fbest)
plot(self.f_list)
return wbest
def train(self):
global fbest,wbest
#初期の子のリスト生成
p_list,c_list,f_list,g_list = [],[],[],[]
for i in range(G):
for w in self.w_list:
p = self.game(w)
if p[1] > fbest:
fbest = p[1]
wbest = p[0]
c_list.append(self.Population(p[0],p[1]))
f_list.append(p[1])
g_list.append(max(f_list))
print(f"{i+1}: {max(f_list)}")
if i < 1:
p_list = copy.copy(c_list)
else:
p_list = [p_list[n] if p_list[n].f > c_list[n].f else c_list[n] for n in range(P)]
self.w_list = self._generate(p_list)
c_list.clear()
#ネットワーク保存
if i == int(G/10) -1: self.save(wbest,i+1)
if i == int(G/2) -1: self.save(wbest,i+1)
print(fbest)
plot(f_list)
return wbest
def main():
os.chdir(
'/Users/ericzhou/Desktop/程序代码/Programming/Visual_Studio/Python/Projects/Lottery'
)
file_name = 'data.csv'
if not os.path.exists('data.csv'):
maxi = input('How many pages of data to use(≤135) > ')
get_history_result(file_name, maxi)
else:
choice = input('Use existing data? (y/n) > ')
if choice == 'n':
maxi = input('How many pages of data to use(≤135) > ')
get_history_result(file_name, maxi)
else:
print('Using existing data......')
print()
choice = input(
'1.Linear Regression\n2.Markov Chain\n3.Manual Work\nChoose which method to use> '
)
try:
if int(choice) == 1:
get_predicted_num(file_name, 'r1', 1, 'R1')
get_predicted_num(file_name, 'r2', 2, 'R2')
get_predicted_num(file_name, 'r3', 3, 'R3')
get_predicted_num(file_name, 'r4', 4, 'R4')
get_predicted_num(file_name, 'r5', 5, 'R5')
get_predicted_num(file_name, 'r6', 6, 'R6')
get_predicted_num(file_name, 'b1', 7, 'B1')
elif int(choice) == 2:
predict_next(file_name, 'r1')
predict_next(file_name, 'r2')
predict_next(file_name, 'r3')
predict_next(file_name, 'r4')
predict_next(file_name, 'r5')
predict_next(file_name, 'r6')
predict_next(file_name, 'b1')
else:
plot(file_name)
except ValueError:
print('Please input a valid number!')
def test_plot(self):
t, wsol = self.scene.sim(5, 10)
plot(wsol, t)
self.scene.set_shape(self.body)
t, wsol = self.scene.sim(3, 40)
plot(wsol, t)
self.scene.set_init_velo(2 * math.cos(math.pi / 2),
2 * math.sin(math.pi / 2))
self.scene.set_unbal_forces(Fx2, Fy2)
self.scene.set_shape(self.triangle)
t, wsol = self.scene.sim(2, 30)
plot(wsol, t)
def plotData(self, widget):
"""
Plot specific data, if widget.get_text() == "" plot the current session
"""
nombre = self.glade.get_object("entry2").get_text()
plot(nombre)
# capturando os valores globais do paralelismo
comm = MPI.COMM_WORLD
size = comm.Get_size()
rank = comm.Get_rank()
# print(sys.argv[2], size)
num_ants = int(int(sys.argv[2]) / size)
# Gerando um Graph
# Graph(num Formigas, Gerações, alpha, beta, rho)
g = Graph(num_ants, int(sys.argv[3]), float(sys.argv[4]), float(sys.argv[5]), float(sys.argv[6]))
# Carrega o grafo do arquivo passado como parametro
points = g.load_from_file(sys.argv[1])
# Imprime a solução
print('Rank: {}'.format(rank))
solution = g.solve()
g.update_global()
results = comm.gather(solution, root=0)
if rank == 0:
results.sort()
# Imprimindo o custo da solução
print("cost =", results[0][0])
# Plotando grafo do caminho encontrado
plot(points, results[0][1], int(sys.argv[7]))
plot2(points, g.pm.matrix, int(sys.argv[7]))
import numpy
from FileHandler import read_from_file
from Kmeans import kmeans
from Plot import plot
from scipy.spatial.distance import cdist
def get_error(dataSet, centroids, labels):
errors = [0 for i in range(len(centroids))]
for i in range(len(labels)):
errors[labels[i]] += numpy.math.sqrt(
(dataSet[i][0] - centroids[labels[i]][0])**2 +
(dataSet[i][1] - centroids[labels[i]][1])**2) / labels.count(
labels[i])
return errors
if __name__ == '__main__':
dataSet = read_from_file("Dataset1.csv")
min_iteration = 15
k = 4
centroids, labels = kmeans(dataSet, k, min_iteration)
plot(labels, dataSet, k)
print(
"for k=" + str(k) +
" the average distance between the cluster center and the data points in that cluster : "
+ str(get_error(dataSet, centroids, labels)))
from Cluster import Cluster as cluster
from Plot import Plot as plot
import numpy as np
from DatabaseOperation import Database as database
import datetime
#print (datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"))
p1 = plot()
def Cluster_Plot_Result(CurrentCluster):
# n_clusters_, labels, X, cluster_belong, cluster_centers_indices
cluster_belong = CurrentCluster.finder(CurrentCluster.labels, len(CurrentCluster.X)-1)
Last_Transaction_Distance, Maximum_Distance, Last_Transaction_Cluster_Centroid = p1.plotClusters(CurrentCluster.n_clusters_, CurrentCluster.labels, CurrentCluster.X, cluster_belong, CurrentCluster.cluster_centers_indices)
CurrentCluster.setDistances(Last_Transaction_Distance, Maximum_Distance, Last_Transaction_Cluster_Centroid)
print(CurrentCluster.Last_Transaction_Distance)
def createClusters(data, title):
p1.title=title
temp_Cluster=cluster(np.asarray(data, dtype=float))
temp_Cluster.makeCluster()
Cluster_Plot_Result(temp_Cluster)
return temp_Cluster
def calculateError(cluster):
if int( cluster.Maximum_Distance) == 0 & int (cluster.Last_Transaction_Distance == 0.0):
error = "F"
elif cluster.Maximum_Distance > cluster.Last_Transaction_Distance:
error = "L"
elif cluster.Maximum_Distance < cluster.Last_Transaction_Distance:
LR_Digits = LR(features_train, labels_train, features_test, labels_test)
kaggle.kaggleize(LR_Digits[0],
path + "/Submission/Predictions/" + str(i) + "/LR.csv")
Training_time.append(LR_Digits[1])
Testing_time.append(LR_Digits[2])
print Training_time, Testing_time
KNN_Digits = KNN(features_train, labels_train, features_test, labels_test)
kaggle.kaggleize(KNN_Digits[0],
path + "/Submission/Predictions/" + str(i) + "/KNN.csv")
Training_time.append(KNN_Digits[1])
Testing_time.append(KNN_Digits[2])
print Training_time, Testing_time
plot(c, Training_time, "Training Time", str(i), path)
plot(c + 3, Testing_time, "Testing Time", str(i), path)
print "K Fold CrossValidation in Progress for " + str(i) + " DataSet"
# call Cross Validation function and saving its return error values in CV variable
CV = kFold(features_train, labels_train, features_test, labels_test, path,
str(i))
#plot a graph of Neighbors vs Errors.
values0 = CV[0].values() # save CrossValidation set Error Values
inds0 = CV[0].keys() # save CrossValidation set number of neighbor values
values1 = CV[1].values() # save Training set Error Values
inds1 = CV[1].keys(
) # save respective number of neighbors for Training set Errors.
plt.figure(c + 10, figsize=(6, 4))
plt.plot(inds0, values0, 'sb-',
game = SnakeGame()
#game_loop
while True:
# get state
state_old = game.get_state()
# get move
action = agent.get_action(state_old)
# perform move and get new state
reward, done, score = game.play_step(action)
state_new = game.get_state()
# train short memory
agent.train_short_memory(state_old, action, reward, state_new, done)
# remember
agent.remember(state_old, action, reward, state_new, done)
if done: #게임이 끝났을때
# train long memory, plot result
game.reset()
agent.n_games += 1
agent.train_long_memory()
plot_scores.append(score)
total_score += score
mean_score = total_score / agent.n_games
plot_mean_scores.append(mean_score)
plot(plot_scores, plot_mean_scores)
def plotData(self,widget):
"""
Plot specific data, if widget.get_text() == "" plot the current session
"""
nombre = self.glade.get_object("entry2").get_text()
plot(nombre)
b2 = BodyT([11, 9, 9, 11], [11, 11, 9, 9], [10, 10, 10, 10])
print(b2.cm_x(), b2.cm_y(), b2.mass(), b2.m_inert())
# cm for b2 should be shifted to 10, 10; b.m_inert() should be equal to b2.m_inert()
lst = [c, t, b, b2]
for s in lst:
print(s.cm_x(), s.cm_y(), s.mass(), s.m_inert())
S = Scene(c, Fx, Fy, 0, 0)
# exercise interface for Scene
S.get_shape()
S.get_unbal_forces()
S.get_init_velo()
# exercise setters for Scene
S.set_shape(c)
S.set_unbal_forces(Fx, Fy)
S.set_init_velo(0, 0)
t, wsol = S.sim(10, 100)
for t1, w1 in zip(t, wsol):
print("t=", t1, "rx=", w1[0], "ry=", w1[1], "vx=", w1[2], "vy=", w1[3])
# extract ry values
print(wsol[0:, 1])
plot(wsol, t)
