def __init__(self, window: 'window', video_source=0, replay=False):
self.window = window
self.window.title(settings.get_config("window_title"))
self.replay = replay
if self.replay:
# Load previous data instead of from camera.
self.data_gen = loadDataset.dataset_generator(
'./dataset/imgs/scissor_frames',
'./dataset/csvs/scissor.csv',
repeat=True)
self.inputDimension = [480, 640, 3]
else:
# open video source (by default this will try to open the computer webcam)
self.video_source = video_source
self.vid = MyVideoCapture(self.video_source)
self.inputDimension = [self.vid.height, self.vid.width, 3]
image_size = settings.get_config("image_input_size")
self.maxListSize = settings.get_config("image_history_length")
self.imgList = [np.zeros(image_size)] * self.maxListSize
# Create a canvas that can fit the above video source size
self.canvas = Canvas(window,
height=self.inputDimension[0],
width=self.inputDimension[1])
self.canvas.pack()
self.photo = None
self.strVar = StringVar(value="None")
self.lblClassification = Label(window,
textvariable=self.strVar,
font=("Helvetica", 16))
self.lblClassification.pack(anchor=CENTER, expand=True)
# Set up frame counters and limiters. No more than (fps) frames per second.
# Also set up label to display current frame rate.
self.fps = settings.get_config("max_fps")
self.fps_counter = window_utils.SimpleFPSCounter()
self.fps_limiter = window_utils.SimpleFPSLimiter(fps=self.fps)
self.fps_value = StringVar()
self.fps_label = Label(window,
textvariable=self.fps_value,
font=("Helvetica", 16))
self.fps_label.pack(anchor=CENTER, expand=True)
# Initialize Tensorflow Models
tf.reset_default_graph()
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
self.session = tf.Session(config=config)
self.model1 = models.model1(image_size, self.maxListSize)
self.model2 = models.model2(image_size)
saver = tf.train.Saver()
saver.restore(
self.session,
os.path.join(os.getcwd(), "savedmodels\\both\\models.ckpt"))
# _main_loop() will "recursively" call itself at most (fps) times per second.
self._main_loop()
self.window.mainloop()
def menu():
print("************IBM Stock Price Predictor**************")
print()
choice = input("""
1: Model 1 - Random Forest Regressor
2: Model 2 - Long Short-Term Memory Neural Network
Please enter your choice: """)
if choice == "1":
model1(X, Y)
elif choice == "2":
model2(X, Y)
else:
print("You must only select either 1 or 2 \nPlease try again")
sys.exit(1)
def initialize_model(model_num):
if model_num == 1:
return models.model1(input_size, num_classes)
elif model_num == 2:
return models.model2(input_size, num_classes)
elif model_num == 3:
return models.model3(input_size, num_classes)
elif model_num == 4:
return models.model4(input_size, num_classes)
elif model_num == 5:
return models.model5(input_size, num_classes)
elif model_num == 6:
return models.model6(input_size, num_classes)
elif model_num == 7:
return models.model7(input_size, num_classes)
def initialize_model(model_num, vocab_size, embed_size):
if model_num == 1:
return models.model1(vocab_size, embed_size)
elif model_num == 2:
return models.model2(vocab_size, embed_size)
elif model_num == 3:
return models.model3(vocab_size, embed_size)
elif model_num == 4:
return models.model4(vocab_size, embed_size)
elif model_num == 5:
return models.model5(vocab_size, embed_size)
elif model_num == 6:
return models.model6(vocab_size, embed_size)
elif model_num == 7:
return models.model7(vocab_size, embed_size)
def initialize_model(model_num):
if model_num == 1:
return models.model1()
elif model_num == 2:
return models.model2()
elif model_num == 3:
return models.model3()
elif model_num == 4:
return models.model4()
elif model_num == 5:
return models.model5()
elif model_num == 6:
return models.model6()
elif model_num == 7:
return models.model7()
a = np.reshape(np.array(([0, 3.584, 1.99])), (1, n)) # intercepts
b = np.zeros((1,m1))
z = np.zeros((n,m1))
d = np.array(([0], [-0.75], [-0.5])) # alternative specific variable coefficients
trainingSets = 100
errors = np.empty([trainingSets, 2])
for i in range(trainingSets):
# sample each price independently and uniformly at random from the interval [0, 10]
pblue = random.uniform(1, 10)
pred = random.uniform(1, 10)
w = np.array(([0], [pblue], [pred]))
# ground truth model
results, prob, utilities = simulate(n, z, w, b, d, a, sample)
# fit model 1
model1Estimate, beta = model1(results, n, pblue, pred) #xxx
# fit model 2
model2Estimate = model2(results, n, pblue, pred) #xxx
#print(model1Estimate)
#print(model2Estimate)
# compute sales
trueShare = math.exp(utilities[0,1])/(1 + math.exp(utilities[0,1]))
model1Share = math.exp(float(model1Estimate[1]))/(1 + math.exp(float(model1Estimate[1])))
model2Share = math.exp(float(model2Estimate[1]))/(1 + math.exp(float(model2Estimate[1])))
# compute errors
errors.itemset((i, 0), math.fabs(trueShare-model1Share)/trueShare)
errors.itemset((i, 1), math.fabs(trueShare-model2Share)/trueShare)
# plot histogram
plotHistogram(errors[:,0], errors[:,1], label1=r'Model 1: $V_j = \beta p_j$', label2=r'Model 2: $V_j = \delta_j p_j$', title='Histogram of relative errors', xlabel='Value', ylabel='Frequency')
plotHistogramII([errors[:,0], errors[:,1]], label=[r'Model 1: $V_j = \beta p_j$', r'Model 2: $V_j = \delta_j p_j$'], title='Histogram of relative errors', xlabel='Value', ylabel='Frequency', bins=20)
print(Xvalid[-1])
print(len(Xtest))
print(Xtest[-1])
print(ytrain)
print(len(ytrain))
print(yvalid)
print(len(yvalid))
#print(ytest)
print(len(ytest))
# define vocabulary size (largest integer value)
vocab_size = len(tokenizer.word_index) + 1
# define model
model = models.model1(vocab_size, max_length)
epochs = 20
log_dir = "logs/"
model_name = "model_1"
fit_dir = log_dir + "fit/" + str(
model_name) + "_" + datetime.datetime.now().strftime("%Y%m%d-%H%M")
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=fit_dir,
histogram_freq=1)
# compile network
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
# fit network
#model.fit(Xtrain, ytrain, epochs=100, validation_data=(Xvalid, yvalid), verbose=2)
schools = rows[1:]
for college in schools:
college[1] = float(college[1])
college[2] = float(college[2])
college[3] = float(college[3])
# X 为当前学生数据 (经度，纬度，排名），为示范数据
X_np = np.array([0.4, 0.2, 0.1]) # np.ndarray格式
X = torch.from_numpy(X_np) # torch.Tensor格式
###################################################################################
'''
流程1：输出预测的大学值
'''
mymodel1 = model1().double()
predict_school = mymodel1(X)
print('predict_school:', predict_school)
###################################################################################
'''
流程2：对于所有的大学，选与流程1输出大学距离最近的前10所大学
'''
# 下面利用流程1的预测大学值，从这些可选大学中再做筛选
distances = [] # 用于存储每所可选大学与预测大学的距离
for school in schools:
school = torch.from_numpy(np.array(school[1:4]))
distances.append(torch.dist(school, predict_school))
def LPV_results(subjectID, pla, fileName, sizeReduction):
"""
Trains LPV model on K0,K1,K3 and predicts K2 for a given model
INPUT:
subjectID -- int
sizeReduction -- int
"""
pd.options.mode.chained_assignment = None
dw = dataloader.load_P00X(subjectID)
bi = 50
rrv_all = []
rrv_split = []
#creating the trial id
k_base = copy.deepcopy(
dw.kinematics.query('TrialID == 0')).reset_index(drop=True)
k_tr = copy.deepcopy(
dw.kinematics.query('TrialID == [0,1,2]')).reset_index(drop=True)
k_tr = pd.concat([
k_tr[k_tr['TrialID'] == 0][:-sizeReduction],
k_tr[k_tr['TrialID'] == 1][:-sizeReduction],
k_tr[k_tr['TrialID'] == 2][:-sizeReduction]
],
ignore_index=True)
k_val = copy.deepcopy(
dw.kinematics.query('TrialID == [3]')).reset_index(drop=True)
#phaser phase estimate
k_base = find_phase(k_base)
k_tr = find_phase(k_tr)
k_val = find_phase(k_val)
k_base = k_base.drop(columns=['pelvis_tx', 'pelvis_ty', 'pelvis_tz'])
k_tr = k_tr.drop(columns=['pelvis_tx', 'pelvis_ty', 'pelvis_tz'])
k_val = k_val.drop(columns=['pelvis_tx', 'pelvis_ty', 'pelvis_tz'])
#model input
Z_base, phi_base, t_base, l_base = models.amp_input_final(k_base)
Z_tr, phi_tr, t_tr, l_tr = models.amp_input_final(k_tr)
Z_val, phi_val, t_val, l_val = models.amp_input_final(k_val)
#Removing the AFO columns in the training set
I_afo = [
l_tr.index('AFO_L'),
l_tr.index('AFO_R'),
l_tr.index('d_AFO_L'),
l_tr.index('d_AFO_R')
]
I = np.array(range(np.shape(Z_tr)[1]))
Z_base = Z_base[:, np.delete(I, I_afo)]
###
# Z_tr = Z_tr[:,np.delete(I,I_afo)]
# Z_val = Z_val[:,np.delete(I,I_afo)]
# l_tr = [l_tr[i] for i in np.delete(I,I_afo)]
# l_val = [l_val[i] for i in np.delete(I,I_afo)]
###
#sample the torques
Z_tr, phi_tr, l_tr = models.sample_torque(Z_tr, phi_tr, l_tr, pla)
Z_val, phi_val, l_val = models.sample_torque(Z_val, phi_val, l_val, pla)
#fs model
f_base = models.fs_model(Z_base, Z_val, phi_base, phi_val, function=True)
#Remove the phase mean
Z_tr[:, np.delete(I, I_afo)] -= np.array([f_base(phi) for phi in phi_tr])
Z_val[:, np.delete(I, I_afo)] -= np.array([f_base(phi) for phi in phi_val])
# Z_tr -= np.array([f_base(phi) for phi in phi_tr])
# Z_val -= np.array([f_base(phi) for phi in phi_val])
#studentize data
mean = np.mean(Z_tr, axis=0)
std = np.std(Z_tr, axis=0)
Z_tr = (Z_tr - mean) / std
Z_val = (Z_val - mean) / std
Y_t, X_t, I_t = util.set_mapper(phi_tr, Z_tr, pla)
Y_v, X_v, I_v = util.set_mapper(phi_val, Z_val, pla)
print('Finding Coefficients')
C = models.model1(Y_t, X_t, phi_tr[I_t])
print('Creating Fourier Series')
fs = models.model1_function(C)
m, n = np.shape(X_v)
Yh = np.zeros((m, n))
print('Predicting')
A = fs(phi_val[I_v])
for i in range(m):
Yh[i, :] = np.dot(util.affenize(X_v[i, :][np.newaxis, :]), A[:, :, i])
#Save RRV statistic
file_name = 'P' + '%03d' % (subjectID) + '_LPV_' + fileName + '_' + str(
28800 - sizeReduction) + '.pckl'
print(
'Average error predicting from initial condition with the average model: '
+ str(np.mean(np.abs(Yh - Y_v))))
RRV = util.bootstrap_rrv(Yh, Y_v, bi)
Nstates = int(len(l_val) / 2)
results = pd.DataFrame({"RRV": np.mean(RRV[:, :Nstates], axis=0)},
index=l_val[:Nstates])
print(results)
results.to_pickle(os.path.join('results', file_name))