def save_nn(nn_obj, path):
"""
save NN object as file
nn_obj, NN
path: str, PATH to the saved file
"""
if isinstance(nn_obj, dict):
prn.saveNN(nn_obj, path)
elif isinstance(nn_obj, Net_tr):
tr.save(nn_obj, path)
else:
print("canceled")
def save_to_file(self, filename='narxNet'):
pr.saveNN(self.net, filename=filename)
###
# Create and train NN
# create feed forward neural network with 1 input, 2 hidden layers with
# 4 neurons each and 1 output
# the NN has a recurrent connection with delay of 1 timesteps from the output
# to the first layer
net = prn.CreateNN([3, 5, 5, 2], dIn=[0], dIntern=[], dOut=[1])
# Train NN with training data P=input and Y=target
# Set maximum number of iterations k_max to 500
# Set termination condition for Error E_stop to 1e-5
# The Training will stop after 500 iterations or when the Error <=E_stop
net = prn.train_LM(P, Y, net, verbose=True, k_max=500, E_stop=1e-5)
###
# Save outputs to certain file
prn.saveNN(net, "D:/School/Masterproef/Python/pyrenn/SavedNN/compair.csv")
print("savegelukt")
###
# Calculate outputs of the trained NN for train and test data
y = prn.NNOut(P, net)
ytest = prn.NNOut(Ptest, net)
time_stop[0] = time.time()
cpu_percent[0] = psutil.cpu_percent()
virtual_mem[0] = psutil.virtual_memory()
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# example_friction.py
time_start[1] = time.time()
def Tranning_by_Neural_Network():
#------------------------------讀檔創建Dataframe---------------------------------
#filepath='C:\\Users\\richard.weng\\Documents\\Python Scripts\\python_projects\\(1) NIVG Project\\ANN\\'
file_data = file_name.get() + '.csv'
df0 = pd.read_csv(file_data)
#選擇受測人
#df = df[df.Name=='Nick']
df = df0.iloc[:, 1:] #移除first column of tester
print(df.T.tail())
print('--------------------------------------------')
print('df 長度為:', len(df))
print('--------------------------------------------')
P = df.T.iloc[1:features_Num.get() + 1, 0:len(df)]
print(P.tail())
print('input的格式:', P.shape)
print('--------------------------------------------')
Y = df.T.iloc[0:1, 0:len(df)]
print(Y.tail())
print('output的格式:', Y.shape)
print('--------------------------------------------')
#轉成2d array
P = np.array(P)
Y = np.array(Y)
# 假設70%訓練，30%要驗證 (TrainingData and TestingData)
x_train, x_test, y_train, y_test = train_test_split(P.T,
Y.T,
test_size=0.3,
random_state=None)
x_of_train = (x_train / np.amax(x_train, axis=0)).T
x_of_test = (x_test / np.amax(x_train, axis=0)).T
y_of_train = y_train.T / 600
y_of_test = y_test.T / 600
#------------------------------讀檔創建Dataframe------------------------------------
#----------------------------ANN 主程式---------------------------------------------
#8 input,2 hidden layer, 3 neuron (create NN)
net = prn.CreateNN([
features_Num.get(),
hiddenlayer1_features.get(),
hiddenlayer2_features.get(), 1
])
# Train by NN
net = prn.train_LM(x_of_train,
y_of_train,
net,
verbose=True,
k_max=iteration.get(),
E_stop=1e-10)
# print out result
y_prn_train = prn.NNOut(x_of_train, net)
y_prn_test = prn.NNOut(x_of_test, net)
# print('x train data 預測的 Predicted Y:','\n',y_prn_train*600)
# print('x test data 預測的 Predicted Y:','\n',y_prn_test*600)
#----------------------------ANN 主程式---------------------------------------------
#----------------------------確認執行後的結果------------------------------------------
# visualize result
plt.scatter(y_of_train * 600, y_prn_train * 600)
plt.scatter(y_of_test * 600, y_prn_test * 600)
plt.title('ANN Simulation Result')
plt.xlabel('Input glucose (mg/dL)')
plt.ylabel('Predicted glucose (mg/dL)')
plt.grid()
plt.show()
print('測試組原本的糖值:', '\n', y_of_test * 600)
print('測試組預測的糖值:', '\n', y_prn_test * 600)
#----------------------------確認執行後的結果------------------------------------------
#Save ANN
prn.saveNN(net, file_name.get() + '_LM_parameter' + '.csv')
#Check final correlation
y_all = prn.NNOut((P.T / np.amax(x_train, axis=0)).T, net) * 600
plt.scatter(Y.flatten(), y_all)
Name = df0['Name'].values.tolist()
df_result = pd.DataFrame({
'Name': Name,
'total_y': Y.flatten(),
'total_pre_y': y_all
})
print('相關性分析:\n', df_result.corr())
#列印出多少數據
print('總共樣本數:', len(df_result))
#Save the new result into new Excel
df_result.to_csv(file_name.get() + '_LM_result' + '.csv')
#
# #create recurrent neural network with 1 input, 2 hidden layers with
# #2 neurons each and 1 output
# #the NN has a recurrent connection with delay of 1 timestep in the hidden
# # layers and a recurrent connection with delay of 1 and 2 timesteps from the output
# # to the first layer
net = prn.CreateNN([22, 11, 1], dIn=[0], dIntern=[], dOut=[1])
#
# #Train NN with training data P=input and Y=target
# #Set maximum number of iterations k_max to 100
# #Set termination condition for Error E_stop to 1e-3
# #The Training will stop after 100 iterations or when the Error <=E_stop
net = prn.train_LM(P, Y, net, verbose=True, k_max=100, E_stop=1e-3)
prn.saveNN(net, "RNNmodel_st.mdl")
print("saved")
os._exit(0)
# print("loading")
# net = prn.loadNN("RNNmodel.mdl")
# print("loaded")
###
#Calculate outputs of the trained NN for train and test data
y = prn.NNOut(P, net)
# for i, o in zip(P, Y):
# output = winner_net.activate(i)
# print("input {!r}, expected output {!r}, got {!r}".format(i, o, output))
# print("expected output {!r}, got {!r}".format(o, output))
# print(y)
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import pyrenn as prn
datosIn = pd.read_csv("pacientestrain.csv", sep=',')
datosOut = pd.read_csv("pacientestarg.csv", sep=',')
P = np.array(datosIn)
Y = np.array(datosOut)
inputs = 7
outputs = 1
net = prn.CreateNN([inputs, 10, 14, outputs],
dIn=[0],
dIntern=[100],
dOut=[10, 20])
net = prn.train_LM(P, Y, net, verbose=True, k_max=20000, E_stop=1e-10)
y = prn.NNOut(P, net)
ytest = prn.NNOut(P, net)
ytest = np.array(ytest)
print(ytest)
filename = "nnBP.csv"
prn.saveNN(net, filename)
Ytest = np.array([df[9], df[10]])
psutil.cpu_percent(interval=None, percpu=True)
time_start.append(time.time())
# Create and train NN
net = prn.CreateNN([3, 5, 5, 2], dIn=[0], dIntern=[], dOut=[1])
net = prn.train_LM(P, Y, net, verbose=True, k_max=500, E_stop=1e-5)
time_stop.append(time.time())
cores.append(psutil.cpu_percent(interval=None, percpu=True))
virtual_mem.append(psutil.virtual_memory())
###
# Save outputs to certain file
prn.saveNN(net, "D:/School/Masterproef/Python/pyrenn/SavedNN/compair.csv")
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# example_friction.py
for i in range(iterations):
time_start.append(time.time())
# Read Example Data
df = genfromtxt('example_data_friction.csv', delimiter=',')
P = df[1]
Y = df[2]
Ptest = df[3]
Ytest = df[4]
psutil.cpu_percent(interval=None, percpu=True)
sourceframes *= pysptk.blackman(frameLength) # windowing
sourcemcepvectors = np.apply_along_axis(
pysptk.mcep, 1, sourceframes, order,
alpha) # extract MCEPs of the source frames
sr, tx = wavfile.read(targetfile)
targetframes = librosa.util.frame(
tx,
frame_length=frameLength, # framing the target audio
hop_length=hop_length).astype(np.float64).T
targetframes *= pysptk.blackman(frameLength) # windowing
targetmcepvectors = np.apply_along_axis(
pysptk.mcep, 1, targetframes, order,
alpha) # extract mceps of target frames
# Normalising for feeding into RNN.
norm = min(len(sourcemcepvectors), len(targetmcepvectors))
transsourcemcepvectorsmod = np.transpose(sourcemcepvectors[0:norm])
transtargetmcepvectorsmod = np.transpose(targetmcepvectors[0:norm])
# Training Model.
net = pyrenn.CreateNN([order + 1, order + 5, order + 5, order + 1])
net = pyrenn.train_LM(transsourcemcepvectorsmod,
transtargetmcepvectorsmod,
net,
k_max=100,
verbose=True,
E_stop=5)
# Saving Model.
pyrenn.saveNN(net, 'pyrennweights_2.csv')
Ytest = np.array([df[9], df[10]])
psutil.cpu_percent(interval=None, percpu=True)
time_start.append(time.time())
# Create and train NN
net = prn.CreateNN([3, 5, 5, 2], dIn=[0], dIntern=[], dOut=[1])
net = prn.train_LM(P, Y, net, verbose=True, k_max=500, E_stop=1e-5)
time_stop.append(time.time())
cores.append(psutil.cpu_percent(interval=None, percpu=True))
virtual_mem.append(psutil.virtual_memory())
###
# Save outputs to certain file
prn.saveNN(net, "./SavedNN/compair.csv")
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# example_friction.py
for i in range(iterations):
time_start.append(time.time())
# Read Example Data
df = genfromtxt('example_data_friction.csv', delimiter=',')
P = df[1]
Y = df[2]
Ptest = df[3]
Ytest = df[4]
psutil.cpu_percent(interval=None, percpu=True)
label='Train sets (70% of data)')
plt.scatter(y_of_test * 600,
y_prn_test * 600,
label='Verify sets (30% of data)')
plt.title('ANN Simulation Result')
plt.xlabel('Input glucose (mg/dL)')
plt.ylabel('Predicted glucose (mg/dL)')
plt.legend()
plt.grid()
plt.show()
print('測試組原本的糖值:', '\n', y_of_test * 600)
print('測試組預測的糖值:', '\n', y_prn_test * 600)
#----------------------------確認執行後的結果------------------------------------------
#Save ANN
prn.saveNN(net, file_name + '_LM_parameter' + '.csv')
#----------------------------確認執行後的結果------------------------------------------
#Check final correlation
y_all = prn.NNOut((P.T / np.amax(x_train, axis=0)).T, net) * 600
plt.scatter(Y.flatten(), y_all)
Name = df0['Name'].values.tolist()
df_result = pd.DataFrame({
'Name': Name,
'total_y': Y.flatten(),
'total_pre_y': y_all
})
print('相關性分析:\n', df_result.corr())
#列印出多少數據
print('總共樣本數:', len(df_result))
# print (y[i][0]);
#z=y;
#np.around(y,0,z);
#for i in range(len(z)):
# print (z[i][0]);
# if z[i][0]==1:
# print diseases[i];
#for i in range(len(disease_map)): not sure
#print disease_map
#ytest = prn.NNOut(Ptest,net)
#prn.NNOut(P, net[, P0=None, Y0=None])
prn.saveNN(net,'/usr/local/lib/python2.7/site-packages/examples/minor_final.csv')
###
#Plot results
#fig = plt.figure(figsize=(15,10))
#ax0 = fig.add_subplot(221)
#ax1 = fig.add_subplot(222,sharey=ax0)
#ax2 = fig.add_subplot(223)
#ax3 = fig.add_subplot(224,sharey=ax2)
#fs=18
#t = np.arange(0,480.0)/4 #480 timesteps in 15 Minute resolution
#Train Data
#ax0.set_title('Train Data',fontsize=fs)
#ax0.plot(y[0],color='b',lw=2,label='NN Output')
#ax0.plot(Y[0],color='r',marker='None',linestyle=':',lw=3,markersize=8,label='Data')
#ax0.tick_params(labelsize=fs-2)