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Lei Shi 2024-03-24 23:42:27 +01:00
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88
keyboard_and_mouse/stan/plot_user.py Normal file
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import numpy as np
from numpy import genfromtxt
import matplotlib.pyplot as plt
from pathlib import Path
import argparse
def main():
parser = argparse.ArgumentParser(description='')
parser.add_argument('--batch_size', type=int, default=8, help='batch size')
parser.add_argument('--lr', type=float, default=1e-4, help='learning rate')
parser.add_argument('--hidden_size', type=int, default=128, help='hidden_size')
parser.add_argument('--model_type', type=str, default='lstmlast', help='model type')
parser.add_argument('--N', type=int, default=1, help='number of sequence for inference')
parser.add_argument('--user', type=int, default=1, help='number of users')
args = parser.parse_args()
plot_type = 'bar' # line bar
width = [-0.3, -0.2, -0.1, 0, 0.1, 0.2, 0.3]
# read data
user_data_list = []
for i in range(args.user):
model_data_list = []
path = "result/"+"N"+ str(args.N) + "/" + args.model_type + "bs_" + str(args.batch_size) + '_lr_' + str(args.lr) + '_hidden_size_' + str(args.hidden_size) + '_N' + str(args.N) + "_result_user" + str(i) +".csv"
data = genfromtxt(path, delimiter=',', skip_header =1)
for j in range(7):
data_temp = data[[1+7*j+j,2+7*j+j,3+7*j+j,4+7*j+j,5+7*j+j,6+7*j+j,7+7*j+j],:][:,[2,4,6,7]]
model_data_list.append(data_temp)
model_data_list = np.concatenate(model_data_list, axis=0)
user_data_list.append(model_data_list)
color = ['royalblue', 'lightgreen', 'tomato', 'indigo', 'plum', 'darkorange', 'blue']
legend = ['rule 1', 'rule 2', 'rule 3', 'rule 4', 'rule 5', 'rule 6', 'rule 7']
fig, axs = plt.subplots(7, sharex=True, sharey=True)
fig.set_figheight(14)
fig.set_figwidth(25)
for ax in range(7):
y_total = []
y_low_total = []
y_high_total = []
for j in range(7):
y= []
y_low = []
y_high = []
for i in range(len(user_data_list)):
y.append(user_data_list[i][j+ax*7][0])
y_low.append(user_data_list[i][j+ax*7][2])
y_high.append(user_data_list[i][j+ax*7][3])
y_total.append(y)
y_low_total.append(y_low)
y_high_total.append(y_high)
print()
print("user mean of mean prob: ", np.mean(y))
print("user mean of sd prob: ", np.std(y))
for i in range(7):
if plot_type == 'line':
axs[ax].plot(range(args.user), y_total[i], color=color[i], label=legend[i])
axs[ax].fill_between(range(args.user), y_low_total[i], y_high_total[i], color=color[i],alpha=0.3 )
if plot_type == 'bar':
width = [-0.36, -0.24, -0.12, 0, 0.12, 0.24, 0.36]
yerror = [np.array(y_total[i])-np.array(y_low_total[i]), np.array(y_high_total[i])-np.array(y_total[i])]
axs[ax].bar(np.arange(args.user)+width[i], y_total[i], width=0.08, yerr=yerror, label=legend[i], color=color[i])
axs[ax].tick_params(axis='x', which='both', length=0)
axs[ax].set_ylabel('prob', fontsize=22)
for k,x in enumerate(np.arange(args.user)+width[i]):
y = y_total[i][k] + yerror[1][k]
axs[ax].annotate(f'{y_total[i][k]:.2f}', (x, y), textcoords='offset points', xytext=(-18,3), fontsize=16)
axs[0].text(-0.1, 0.9, 'True Intention:', horizontalalignment='center', verticalalignment='center', transform=axs[0].transAxes, fontsize= 22) # all: -0.3,0.5 3rows: -0.5,0.5
axs[ax].text(-0.1, 0.5, legend[ax], horizontalalignment='center', verticalalignment='center', transform=axs[ax].transAxes, fontsize= 22, color=color[ax])
axs[ax].tick_params(axis='both', which='major', labelsize=16)
plt.xticks(range(args.user),('1', '2', '3', '4', '5'))
plt.xlabel('user', fontsize= 22)
handles, labels = axs[0].get_legend_handles_labels()
plt.ylim([0, 1])
Path("figure").mkdir(parents=True, exist_ok=True)
if plot_type == 'line':
plt.savefig("figure/"+"N"+ str(args.N) + "_ "+ args.model_type + "_bs_" + str(args.batch_size) + '_lr_' + str(args.lr) + '_hidden_size_' + str(args.hidden_size) + '_N' + str(args.N) + "_line.png", bbox_inches='tight')
if plot_type == 'bar':
plt.savefig("figure/"+"N"+ str(args.N) + "_ "+ args.model_type + "_bs_" + str(args.batch_size) + '_lr_' + str(args.lr) + '_hidden_size_' + str(args.hidden_size) + '_N' + str(args.N) + "_bar.png", bbox_inches='tight')
plt.show()
if __name__ == '__main__':
main()

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keyboard_and_mouse/stan/plot_user.sh Normal file
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python3 plot_user.py \
--model_type lstmlast_ \
--batch_size 8 \
--lr 1e-4 \
--hidden_size 128 \
--N 1 \
--user 5

99
keyboard_and_mouse/stan/plot_user_all_individual.py Normal file
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import numpy as np
from numpy import genfromtxt
import matplotlib.pyplot as plt
import argparse
def main():
parser = argparse.ArgumentParser(description='')
parser.add_argument('--batch_size', type=int, default=8, help='batch size')
parser.add_argument('--lr', type=float, default=1e-4, help='learning rate')
parser.add_argument('--hidden_size', type=int, default=128, help='hidden_size')
parser.add_argument('--model_type', type=str, default='lstmlast', help='model type')
parser.add_argument('--N', type=int, default=1, help='number of sequence for inference')
parser.add_argument('--user', type=int, default=1, help='number of users')
args = parser.parse_args()
plot_type = 'bar' # line bar
act_series = 5
# read data
plot_list = []
for act in range(1,act_series+1):
user_data_list = []
for i in range(args.user):
model_data_list = []
path = "result/"+"N"+ str(args.N) + "/" + args.model_type + "bs_" + str(args.batch_size) + '_lr_' + str(args.lr) + '_hidden_size_' + str(args.hidden_size) + '_N' + str(args.N) + "_result_user" + str(i) + "_rate__100" + "_act_" + str(act) +".csv"
data = genfromtxt(path, delimiter=',', skip_header =1)
for j in range(7):
data_temp = data[[1+7*j+j,2+7*j+j,3+7*j+j,4+7*j+j,5+7*j+j,6+7*j+j,7+7*j+j],:][:,[2,4,6,7]]
model_data_list.append(data_temp)
model_data_list = np.concatenate(model_data_list, axis=0)
print(model_data_list.shape)
user_data_list.append(model_data_list)
color = ['royalblue', 'lightgreen', 'tomato', 'indigo', 'plum', 'darkorange', 'blue']
legend = ['rule 1', 'rule 2', 'rule 3', 'rule 4', 'rule 5', 'rule 6', 'rule 7']
fig, axs = plt.subplots(7, sharex=True, sharey=True)
fig.set_figheight(14)
fig.set_figwidth(25)
for ax in range(7):
y_total = []
y_low_total = []
y_high_total = []
for j in range(7):
y= []
y_low = []
y_high = []
for i in range(len(user_data_list)):
y.append(user_data_list[i][j+ax*7][0])
y_low.append(user_data_list[i][j+ax*7][2])
y_high.append(user_data_list[i][j+ax*7][3])
y_total.append(y)
y_low_total.append(y_low)
y_high_total.append(y_high)
print()
print("user mean of mean prob: ", np.mean(y))
print("user mean of sd prob: ", np.std(y))
for i in range(7):
if plot_type == 'line':
axs[ax].plot(range(args.user), y_total[i], color=color[i], label=legend[i])
axs[ax].fill_between(range(args.user), y_low_total[i], y_high_total[i], color=color[i],alpha=0.3 )
if plot_type == 'bar':
width = [-0.36, -0.24, -0.12, 0, 0.12, 0.24, 0.36]
yerror = [np.array(y_total[i])-np.array(y_low_total[i]), np.array(y_high_total[i])-np.array(y_total[i])]
axs[ax].bar(np.arange(args.user)+width[i], y_total[i], width=0.08, yerr=[np.array(y_total[i])-np.array(y_low_total[i]), np.array(y_high_total[i])-np.array(y_total[i])], label=legend[i], color=color[i])
axs[ax].tick_params(axis='x', which='both', length=0)
axs[ax].set_ylabel('prob', fontsize=36) # was 22,
for k,x in enumerate(np.arange(args.user)+width[i]):
y = y_total[i][k] + yerror[1][k]
axs[ax].annotate(f'{y_total[i][k]:.2f}', (x, y), textcoords='offset points', xytext=(-18,3), fontsize=16) #was 16
axs[0].text(-0.17, 1.2, 'True Intention:', horizontalalignment='center', verticalalignment='center', transform=axs[0].transAxes, fontsize= 46) # was -0.1 0.9 25
axs[ax].text(-0.17, 0.5, legend[ax], horizontalalignment='center', verticalalignment='center', transform=axs[ax].transAxes, fontsize= 46, color=color[ax]) # was 25
axs[ax].tick_params(axis='both', which='major', labelsize=42) # was 18
for tick in axs[ax].xaxis.get_major_ticks():
tick.set_pad(20)
plt.xticks(range(args.user),('1', '2', '3', '4', '5'))
plt.xlabel('user', fontsize= 42) # was 22
handles, labels = axs[0].get_legend_handles_labels()
plt.ylim([0, 1])
plt.tight_layout()
if plot_type == 'line':
plt.savefig("figure/"+"N"+ str(args.N) + "_ "+ args.model_type + "_bs_" + str(args.batch_size) + '_lr_' + str(args.lr) + '_hidden_size_' + str(args.hidden_size) + '_N' + str(args.N) + "_act_series" + str(act) + "_line_all_individual.png", bbox_inches='tight')
if plot_type == 'bar':
plt.savefig("figure/"+"N"+ str(args.N) + "_ "+ args.model_type + "_bs_" + str(args.batch_size) + '_lr_' + str(args.lr) + '_hidden_size_' + str(args.hidden_size) + '_N' + str(args.N) + "_act_series" + str(act) + "_bar_all_individual.png", bbox_inches='tight')
if plot_type == 'line':
plt.savefig("figure/"+"N"+ str(args.N) + "_ "+ args.model_type + "_bs_" + str(args.batch_size) + '_lr_' + str(args.lr) + '_hidden_size_' + str(args.hidden_size) + '_N' + str(args.N) + "_act_series" + str(act) + "_line_all_individual.eps", bbox_inches='tight', format='eps')
if plot_type == 'bar':
plt.savefig("figure/"+"N"+ str(args.N) + "_ "+ args.model_type + "_bs_" + str(args.batch_size) + '_lr_' + str(args.lr) + '_hidden_size_' + str(args.hidden_size) + '_N' + str(args.N) + "_act_series" + str(act) + "_bar_all_individual.eps", bbox_inches='tight', format='eps')
#plt.show()
if __name__ == '__main__':
main()

8
keyboard_and_mouse/stan/plot_user_all_individual.sh Normal file
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python3 plot_user_all_individual.py \
--model_type lstmlast_ \
--batch_size 8 \
--lr 1e-4 \
--hidden_size 128 \
--N 1 \
--user 5

93
keyboard_and_mouse/stan/plot_user_all_individual_chiw.py Normal file
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import numpy as np
from numpy import genfromtxt
import matplotlib.pyplot as plt
model_type = "lstmlast_"
batch_size = 8
lr = 1e-4
hidden_size = 128
N = 1
user = 5
plot_type = 'bar' # line bar
act_series = 5
# read data
plot_list = []
for act in range(1,act_series+1):
user_data_list = []
for i in range(user):
model_data_list = []
path = "result/"+"N"+ str(N) + "/" + model_type + "bs_" + str(batch_size) + '_lr_' + str(lr) + '_hidden_size_' + str(hidden_size) + '_N' + str(N) + "_result_user" + str(i) + "_rate__100" + "_act_" + str(act) +".csv"
data = genfromtxt(path, delimiter=',', skip_header =1)
for j in range(7):
data_temp = data[[1+7*j+j,2+7*j+j,3+7*j+j,4+7*j+j,5+7*j+j,6+7*j+j,7+7*j+j],:][:,[2,4,6,7]]
model_data_list.append(data_temp)
model_data_list = np.concatenate(model_data_list, axis=0)
print(model_data_list.shape)
user_data_list.append(model_data_list)
color = ['royalblue', 'lightgreen', 'tomato', 'indigo', 'plum', 'darkorange', 'blue']
legend = ['rule 1', 'rule 2', 'rule 3', 'rule 4', 'rule 5', 'rule 6', 'rule 7']
fig, axs = plt.subplots(7, sharex=True, sharey=True)
fig.set_figheight(14)
fig.set_figwidth(25)
for ax in range(7):
y_total = []
y_low_total = []
y_high_total = []
for j in range(7):
y= []
y_low = []
y_high = []
for i in range(len(user_data_list)):
y.append(user_data_list[i][j+ax*7][0])
y_low.append(user_data_list[i][j+ax*7][2])
y_high.append(user_data_list[i][j+ax*7][3])
y_total.append(y)
y_low_total.append(y_low)
y_high_total.append(y_high)
print()
print(legend[ax])
print("user mean of mean prob: ", np.mean(y))
print("user mean of sd prob: ", np.std(y))
for i in range(7):
if plot_type == 'line':
axs[ax].plot(range(user), y_total[i], color=color[i], label=legend[i])
axs[ax].fill_between(range(user), y_low_total[i], y_high_total[i], color=color[i],alpha=0.3 )
if plot_type == 'bar':
width = [-0.36, -0.24, -0.12, 0, 0.12, 0.24, 0.36]
yerror = [np.array(y_total[i])-np.array(y_low_total[i]), np.array(y_high_total[i])-np.array(y_total[i])]
axs[ax].bar(np.arange(user)+width[i], y_total[i], width=0.08, yerr=[np.array(y_total[i])-np.array(y_low_total[i]), np.array(y_high_total[i])-np.array(y_total[i])], label=legend[i], color=color[i])
axs[ax].tick_params(axis='x', which='both', length=0)
axs[ax].set_ylabel('prob', fontsize=26) # was 22,
axs[ax].set_title(legend[ax], color=color[ax], fontsize=26)
for k,x in enumerate(np.arange(user)+width[i]):
y = y_total[i][k] + yerror[1][k]
axs[ax].annotate(f'{y_total[i][k]:.2f}', (x, y), textcoords='offset points', xytext=(-18,3), fontsize=16) #was 16
#axs[0].text(-0.17, 1.2, 'True Intention:', horizontalalignment='center', verticalalignment='center', transform=axs[0].transAxes, fontsize= 46) # was -0.1 0.9 25
#axs[ax].text(-0.17, 0.5, legend[ax], horizontalalignment='center', verticalalignment='center', transform=axs[ax].transAxes, fontsize= 46, color=color[ax]) # was 25
axs[ax].tick_params(axis='both', which='major', labelsize=18) # was 18
for tick in axs[ax].xaxis.get_major_ticks():
tick.set_pad(20)
plt.xticks(range(user),('1', '2', '3', '4', '5'))
plt.xlabel('user', fontsize= 26) # was 22
handles, labels = axs[0].get_legend_handles_labels()
plt.ylim([0, 1.2])
plt.tight_layout()
if plot_type == 'line':
plt.savefig("figure/"+"N"+ str(N) + "_ "+ model_type + "_bs_" + str(batch_size) + '_lr_' + str(lr) + '_hidden_size_' + str(hidden_size) + '_N' + str(N) + "_act_series" + str(act) + "_line_all_individual_chiw.png", bbox_inches='tight')
if plot_type == 'bar':
plt.savefig("figure/"+"N"+ str(N) + "_ "+ model_type + "_bs_" + str(batch_size) + '_lr_' + str(lr) + '_hidden_size_' + str(hidden_size) + '_N' + str(N) + "_act_series" + str(act) + "_bar_all_individual_chiw.png", bbox_inches='tight')
#plt.show()
if plot_type == 'line':
plt.savefig("figure/"+"N"+ str(N) + "_ "+ model_type + "_bs_" + str(batch_size) + '_lr_' + str(lr) + '_hidden_size_' + str(hidden_size) + '_N' + str(N) + "_act_series" + str(act) + "_line_all_individual_chiw.eps", bbox_inches='tight', format='eps')
if plot_type == 'bar':
plt.savefig("figure/"+"N"+ str(N) + "_ "+ model_type + "_bs_" + str(batch_size) + '_lr_' + str(lr) + '_hidden_size_' + str(hidden_size) + '_N' + str(N) + "_act_series" + str(act) + "_bar_all_individual_chiw.eps", bbox_inches='tight', format='eps')

88
keyboard_and_mouse/stan/plot_user_length_10_steps.py Normal file
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import numpy as np
from numpy import genfromtxt
import matplotlib.pyplot as plt
import argparse
def main():
parser = argparse.ArgumentParser(description='')
parser.add_argument('--batch_size', type=int, default=8, help='batch size')
parser.add_argument('--lr', type=float, default=1e-4, help='learning rate')
parser.add_argument('--hidden_size', type=int, default=128, help='hidden_size')
parser.add_argument('--model_type', type=str, default='lstmlast', help='model type')
parser.add_argument('--N', type=int, default=1, help='number of sequence for inference')
parser.add_argument('--user', type=int, default=1, help='number of users')
args = parser.parse_args()
width = [-0.3, -0.2, -0.1, 0, 0.1, 0.2, 0.3]
rate_user_data_list = []
for r in range(0,101,10): # rate = range(0,101,10)
# read data
user_data_list = []
for i in range(args.user):
model_data_list = []
path = "result/"+"N"+ str(args.N) + "/" + args.model_type + "bs_" + str(args.batch_size) + '_lr_' + str(args.lr) + '_hidden_size_' + str(args.hidden_size) + '_N' + str(args.N) + "_result_user" + str(i) + "_rate__" + str(r) +".csv"
data = genfromtxt(path, delimiter=',', skip_header =1)
for j in range(7):
data_temp = data[[1+7*j+j,2+7*j+j,3+7*j+j,4+7*j+j,5+7*j+j,6+7*j+j,7+7*j+j],:][:,[2,4,6,7]]
model_data_list.append(data_temp)
model_data_list = np.concatenate(model_data_list, axis=0)
if i == 4:
print(model_data_list.shape, model_data_list)
user_data_list.append(model_data_list)
model_data_list_total = np.stack(user_data_list)
print(model_data_list_total.shape)
mean_user_data = np.mean(model_data_list_total,axis=0)
print(mean_user_data.shape)
rate_user_data_list.append(mean_user_data)
color = ['royalblue', 'lightgreen', 'tomato', 'indigo', 'plum', 'darkorange', 'blue']
legend = ['rule 1', 'rule 2', 'rule 3', 'rule 4', 'rule 5', 'rule 6', 'rule 7']
fig, axs = plt.subplots(7, sharex=True, sharey=True)
fig.set_figheight(10) # all sample rate: 10; 3 row: 8
fig.set_figwidth(20)
for ax in range(7):
y_total = []
y_low_total = []
y_high_total = []
for j in range(7):
y= []
y_low = []
y_high = []
for i in range(len(rate_user_data_list)):
y.append(rate_user_data_list[i][j+ax*7][0])
y_low.append(rate_user_data_list[i][j+ax*7][2])
y_high.append(rate_user_data_list[i][j+ax*7][3])
y_total.append(y)
y_low_total.append(y_low)
y_high_total.append(y_high)
print()
print("user mean of mean prob: ", np.mean(y))
print("user mean of sd prob: ", np.std(y))
for i in range(7):
axs[ax].plot(range(0,101,10), y_total[i], color=color[i], label=legend[i])
axs[ax].fill_between(range(0,101,10), y_low_total[i], y_high_total[i], color=color[i],alpha=0.3 )
axs[ax].set_xticks(range(0,101,10))
axs[ax].set_ylabel('prob', fontsize=20)
axs[0].text(-0.125, 0.9, 'True Intention:', horizontalalignment='center', verticalalignment='center', transform=axs[0].transAxes, fontsize= 20)
axs[ax].text(-0.125, 0.5, legend[ax], horizontalalignment='center', verticalalignment='center', transform=axs[ax].transAxes, fontsize= 20, color=color[ax])
axs[ax].tick_params(axis='both', which='major', labelsize=16)
plt.xlabel('Percentage of occurred actions in one action sequence', fontsize= 20)
handles, labels = axs[0].get_legend_handles_labels()
plt.xlim([0, 101])
plt.ylim([0, 1])
plt.savefig("figure/"+"N"+ str(args.N) + "_ "+ args.model_type + "_bs_" + str(args.batch_size) + '_lr_' + str(args.lr) + '_hidden_size_' + str(args.hidden_size) + '_N' + str(args.N) + "_rate_full.png", bbox_inches='tight')
plt.show()
if __name__ == '__main__':
main()

8
keyboard_and_mouse/stan/plot_user_length_10_steps.sh Normal file
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python3 plot_user_length_10_steps.py \
--model_type lstmlast_ \
--batch_size 8 \
--lr 1e-4 \
--hidden_size 128 \
--N 1 \
--user 5

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keyboard_and_mouse/stan/plot_user_length_10_steps_all_individual.py Normal file
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import numpy as np
from numpy import genfromtxt
import matplotlib.pyplot as plt
import argparse
def main():
parser = argparse.ArgumentParser(description='')
parser.add_argument('--batch_size', type=int, default=8, help='batch size')
parser.add_argument('--lr', type=float, default=1e-4, help='learning rate')
parser.add_argument('--hidden_size', type=int, default=128, help='hidden_size')
parser.add_argument('--model_type', type=str, default='lstmlast', help='model type')
parser.add_argument('--N', type=int, default=1, help='number of sequence for inference')
parser.add_argument('--user', type=int, default=1, help='number of users')
args = parser.parse_args()
width = [-0.3, -0.2, -0.1, 0, 0.1, 0.2, 0.3]
act_series = 5
for act in range(1,act_series+1):
rate_user_data_list = []
for r in range(0,101,10):
# read data
user_data_list = []
for i in range(args.user):
model_data_list = []
path = "result/"+"N"+ str(args.N) + "/" + args.model_type + "bs_" + str(args.batch_size) + '_lr_' + str(args.lr) + '_hidden_size_' + str(args.hidden_size) + '_N' + str(args.N) + "_result_user" + str(i) + "_rate__" + str(r) + "_act_" + str(act) +".csv"
data = genfromtxt(path, delimiter=',', skip_header =1)
for j in range(7):
data_temp = data[[1+7*j+j,2+7*j+j,3+7*j+j,4+7*j+j,5+7*j+j,6+7*j+j,7+7*j+j],:][:,[2,4,6,7]]
model_data_list.append(data_temp)
model_data_list = np.concatenate(model_data_list, axis=0)
user_data_list.append(model_data_list)
model_data_list_total = np.stack(user_data_list)
mean_user_data = np.mean(model_data_list_total,axis=0)
rate_user_data_list.append(mean_user_data)
color = ['royalblue', 'lightgreen', 'tomato', 'indigo', 'plum', 'darkorange', 'blue']
legend = ['rule 1', 'rule 2', 'rule 3', 'rule 4', 'rule 5', 'rule 6', 'rule 7']
fig, axs = plt.subplots(7, sharex=True, sharey=True)
fig.set_figheight(14) # was 10
fig.set_figwidth(20)
for ax in range(7):
y_total = []
y_low_total = []
y_high_total = []
for j in range(7):
y= []
y_low = []
y_high = []
for i in range(len(rate_user_data_list)):
y.append(rate_user_data_list[i][j+ax*7][0])
y_low.append(rate_user_data_list[i][j+ax*7][2])
y_high.append(rate_user_data_list[i][j+ax*7][3])
y_total.append(y)
y_low_total.append(y_low)
y_high_total.append(y_high)
print()
print("user mean of mean prob: ", np.mean(y))
print("user mean of sd prob: ", np.std(y))
for i in range(7):
axs[ax].plot(range(0,101,10), y_total[i], color=color[i], label=legend[i])
axs[ax].fill_between(range(0,101,10), y_low_total[i], y_high_total[i], color=color[i],alpha=0.3 )
axs[ax].set_xticks(range(0,101,10))
axs[ax].set_ylabel('prob', fontsize=26) # was 20
axs[0].text(-0.15, 1.2, 'True Intention:', horizontalalignment='center', verticalalignment='center', transform=axs[0].transAxes, fontsize= 36) # was -0.125 20
axs[ax].text(-0.15, 0.5, legend[ax], horizontalalignment='center', verticalalignment='center', transform=axs[ax].transAxes, fontsize= 36, color=color[ax]) # -0.125 20
axs[ax].tick_params(axis='y', which='major', labelsize=24) # was 16
axs[ax].tick_params(axis='x', which='major', labelsize=24) # was 16
for tick in axs[ax].xaxis.get_major_ticks():
tick.set_pad(20)
plt.xlabel('Percentage of occurred actions in one action sequence', fontsize= 36) # was 20
handles, labels = axs[0].get_legend_handles_labels()
plt.xlim([0, 101])
plt.ylim([0, 1])
plt.savefig("figure/"+"N"+ str(args.N) + "_ "+ args.model_type + "_bs_" + str(args.batch_size) + '_lr_' + str(args.lr) + '_hidden_size_' + str(args.hidden_size) + '_N' + str(args.N) + "_act_series" + str(act) + "_rate_ful_all_individuall.png", bbox_inches='tight')
#plt.show()
if __name__ == '__main__':
main()

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keyboard_and_mouse/stan/plot_user_length_10_steps_all_individual.sh Normal file
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python3 plot_user_length_10_steps_all_individual.py \
--model_type lstmlast_ \
--batch_size 8 \
--lr 1e-4 \
--hidden_size 128 \
--N 1 \
--user 5

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keyboard_and_mouse/stan/plot_user_length_10_steps_all_individual_chiw.py Normal file
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import numpy as np
from numpy import genfromtxt
import matplotlib.pyplot as plt
model_type = "lstmlast_"
batch_size = 8
lr = 1e-4
hidden_size = 128
N = 1
user = 5
width = [-0.3, -0.2, -0.1, 0, 0.1, 0.2, 0.3]
act_series = 5
for act in range(1,act_series+1):
rate_user_data_list = []
for r in range(0,101,10):
# read data
print(r)
user_data_list = []
for i in range(user):
model_data_list = []
path = "result/"+"N"+ str(N) + "/" + model_type + "bs_" + str(batch_size) + '_lr_' + str(lr) + '_hidden_size_' + str(hidden_size) + '_N' + str(N) + "_result_user" + str(i) + "_rate__" + str(r) + "_act_" + str(act) +".csv"
data = genfromtxt(path, delimiter=',', skip_header =1)
for j in range(7):
data_temp = data[[1+7*j+j,2+7*j+j,3+7*j+j,4+7*j+j,5+7*j+j,6+7*j+j,7+7*j+j],:][:,[2,4,6,7]]
model_data_list.append(data_temp)
model_data_list = np.concatenate(model_data_list, axis=0)
user_data_list.append(model_data_list)
model_data_list_total = np.stack(user_data_list)
print(model_data_list_total.shape)
mean_user_data = np.mean(model_data_list_total,axis=0)
print(mean_user_data.shape)
rate_user_data_list.append(mean_user_data)
color = ['royalblue', 'lightgreen', 'tomato', 'indigo', 'plum', 'darkorange', 'blue']
legend = ['rule 1', 'rule 2', 'rule 3', 'rule 4', 'rule 5', 'rule 6', 'rule 7']
fig, axs = plt.subplots(7, sharex=True, sharey=True)
fig.set_figheight(14) # was 10
fig.set_figwidth(20)
for ax in range(7):
y_total = []
y_low_total = []
y_high_total = []
for j in range(7):
y= []
y_low = []
y_high = []
for i in range(len(rate_user_data_list)):
y.append(rate_user_data_list[i][j+ax*7][0])
y_low.append(rate_user_data_list[i][j+ax*7][2])
y_high.append(rate_user_data_list[i][j+ax*7][3])
y_total.append(y)
y_low_total.append(y_low)
y_high_total.append(y_high)
print()
print(legend[ax])
print("user mean of mean prob: ", np.mean(y))
print("user mean of sd prob: ", np.std(y))
for i in range(7):
axs[ax].plot(range(0,101,10), y_total[i], color=color[i], label=legend[i])
axs[ax].fill_between(range(0,101,10), y_low_total[i], y_high_total[i], color=color[i],alpha=0.3 )
axs[ax].set_xticks(range(0,101,10))
axs[ax].set_ylabel('prob', fontsize=26) # was 20
axs[ax].set_title(legend[ax], color=color[ax], fontsize=26)
#axs[0].text(-0.15, 1.2, 'True Intention:', horizontalalignment='center', verticalalignment='center', transform=axs[0].transAxes, fontsize= 36) # was -0.125 20
#axs[ax].text(-0.15, 0.5, legend[ax], horizontalalignment='center', verticalalignment='center', transform=axs[ax].transAxes, fontsize= 36, color=color[ax]) # -0.125 20
axs[ax].tick_params(axis='y', which='major', labelsize=18) # was 16
axs[ax].tick_params(axis='x', which='major', labelsize=18) # was 16
for tick in axs[ax].xaxis.get_major_ticks():
tick.set_pad(20)
plt.xlabel('Percentage of occurred actions in one action sequence', fontsize= 26) # was 20
handles, labels = axs[0].get_legend_handles_labels()
plt.xlim([0, 101])
plt.ylim([0, 1.1])
plt.tight_layout()
plt.savefig("figure/"+"N"+ str(N) + "_ "+ model_type + "_bs_" + str(batch_size) + '_lr_' + str(lr) + '_hidden_size_' + str(hidden_size) + '_N' + str(N) + "_act_series" + str(act) + "_rate_ful_all_individuall_chiw.png", bbox_inches='tight')
#plt.show()

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keyboard_and_mouse/stan/strategy_inference_model Executable file
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keyboard_and_mouse/stan/strategy_inference_model.stan Executable file
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data {
int<lower=1> I; // number of question options (22)
int<lower=0> N; // number of questions being asked by the user
int<lower=1> K; // number of strategies
// observed "true" questions of the user
int q[N];
// array of predicted probabilities of questions given strategies
// coming from the forward neural network
matrix[I, K] P_q_S[N];
}
parameters {
// probabiliy vector of the strategies being applied by the user
// to be inferred by the model here
simplex[K] P_S;
}
model {
for (n in 1:N) {
// marginal probability vector of the questions being asked
vector[I] theta = P_q_S[n] * P_S;
// categorical likelihood
target += categorical_lpmf(q[n] | theta);
}
// priors
target += dirichlet_lpdf(P_S | rep_vector(1.0, K));
}

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keyboard_and_mouse/stan/strategy_inference_test.R Normal file
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library(tidyverse)
library(cmdstanr)
library(dplyr)
model_type <- "lstmlast"
batch_size <- "8"
lr <- "0.0001"
hidden_size <- "128"
model_type <- paste0(model_type, "_bs_", batch_size, "_lr_", lr, "_hidden_size_", hidden_size)
print(model_type)
set.seed(9736734)
user_num <- 5
user <-c(0:(user_num-1))
strategies <- c(0:6) # 7 tasks
print(strategies)
print(length(strategies))
N <- 1
# read data from csv
sel <- vector("list", length(strategies))
for (u in seq_along(user)){
dat <- vector("list", length(strategies))
print(paste0('user: ', u))
for (i in seq_along(strategies)) {
dat[[i]] <- read.csv(paste0("../prediction/task", strategies[[i]], "/", model_type, "/user", user[[u]], "_pred", ".csv"))
dat[[i]]$assumed_strategy <- strategies[[i]]
dat[[i]]$index <- dat[[i]]$action_id # sample based on intention
dat[[i]]$id <- dat[[i]][,1] # sample based on intention
}
# reset N after inference
N = 1
# select one action series from one intention
if (user[[u]] == 0){
sel[[1]]<-dat[[1]] %>%
group_by(task_name) %>%
sample_n(N)
sel[[1]] <- data.frame(sel[[1]])
}
# filter data from the selected action series, N series per intention
for (i in seq_along(strategies)) {
dat[[i]]<-subset(dat[[i]], dat[[i]]$action_id == sel[[1]]$action_id[1])
}
row.names(dat) <- NULL
# create save path
dir.create(file.path("result"), showWarnings = FALSE)
dir.create(file.path(paste0("result/", "N", N)), showWarnings = FALSE)
save_path <- paste0("result/", "N", N, "/", model_type, "_N", N, "_", "result","_user", user[[u]], ".csv")
dat <- do.call(rbind, dat) %>%
mutate(index = as.numeric(as.factor(id))) %>%
rename(true_strategy = task_name) %>%
mutate(
true_strategy = factor(
true_strategy, levels = 0:6,
labels = strategies
),
q_type = case_when(
gt %in% c(3,4,5) ~ 0,
gt %in% c(1,2,3,4,5,6,7) ~ 1,
gt %in% c(1,2,3,4) ~ 2,
gt %in% c(1,4,5,6,7) ~ 3,
gt %in% c(1,2,3,6,7) ~ 4,
gt %in% c(2,3,4,5,6,7) ~ 5,
gt %in% c(1,2,3,4,5,6,7) ~ 6,
)
)
dat_obs <- dat %>% filter(assumed_strategy == strategies[[i]])
N <- nrow(dat_obs)
print(c("N: ", N))
q <- dat_obs$gt
true_strategy <- dat_obs$true_strategy
K <- length(unique(dat$assumed_strategy))
print(c("K: ", K))
I <- 7
P_q_S <- array(dim = c(N, I, K))
for (n in 1:N) {
#print(n)
P_q_S[n, , ] <- dat %>%
filter(index == n) %>%
select(matches("^act[[:digit:]]+$")) %>%
as.matrix() %>%
t()
for (k in 1:K) {
# normalize probabilities
P_q_S[n, , k] <- P_q_S[n, , k] / sum(P_q_S[n, , k])
}
}
print(c('dim P_q_S',dim(P_q_S)))
mod <- cmdstan_model("strategy_inference_model.stan")
sub <- which(true_strategy == 0) # "0"
print(c('sub', sub))
sdata <- list(N = length(sub), K = K, I = I, q = q[sub], P_q_S = P_q_S[sub, , ])
fit_0 <- mod$sample(data = sdata, refresh=0, output_dir=paste0(getwd(),"/temp"))
print(fit_0$summary(NULL, c("mean","sd")))
sub <- which(true_strategy == 1)
print(c('sub', sub))
sdata <- list(N = length(sub), K = K, I = I, q = q[sub], P_q_S = P_q_S[sub, , ])
fit_1 <- mod$sample(data = sdata, refresh=0, output_dir=paste0(getwd(),"/temp"))
print(fit_1$summary(NULL, c("mean","sd")))
sub <- which(true_strategy == 2)
print(c('sub', sub))
sdata <- list(N = length(sub), K = K, I = I, q = q[sub], P_q_S = P_q_S[sub, , ])
fit_2 <- mod$sample(data = sdata, refresh=0, output_dir=paste0(getwd(),"/temp"))
print(fit_2$summary(NULL, c("mean","sd")))
sub <- which(true_strategy == 3)
print(c('sub', sub))
sdata <- list(N = length(sub), K = K, I = I, q = q[sub], P_q_S = P_q_S[sub, , ])
fit_3 <- mod$sample(data = sdata, refresh=0, output_dir=paste0(getwd(),"/temp"))
print(fit_3$summary(NULL, c("mean","sd")))
sub <- which(true_strategy == 4)
print(c('sub', sub))
sdata <- list(N = length(sub), K = K, I = I, q = q[sub], P_q_S = P_q_S[sub, , ])
fit_4 <- mod$sample(data = sdata, refresh=0, output_dir=paste0(getwd(),"/temp"))
print(fit_4$summary(NULL, c("mean","sd")))
sub <- which(true_strategy == 5)
print(c('sub', sub))
sdata <- list(N = length(sub), K = K, I = I, q = q[sub], P_q_S = P_q_S[sub, , ])
fit_5 <- mod$sample(data = sdata, refresh=0, output_dir=paste0(getwd(),"/temp"))
print(fit_5$summary(NULL, c("mean","sd")))
sub <- which(true_strategy == 6)
print(c('sub', sub))
sdata <- list(N = length(sub), K = K, I = I, q = q[sub], P_q_S = P_q_S[sub, , ])
fit_6 <- mod$sample(data = sdata, refresh=0, output_dir=paste0(getwd(),"/temp"))
print(fit_6$summary(NULL, c("mean","sd")))
# save csv
df <-rbind(fit_0$summary(), fit_1$summary(), fit_2$summary(), fit_3$summary(), fit_4$summary(), fit_5$summary(), fit_6$summary())
write.csv(df,file=save_path,quote=FALSE)
}

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library(tidyverse)
library(cmdstanr)
library(dplyr)
# using every action sequence from each user
model_type <- "lstmlast"
batch_size <- "8"
lr <- "0.0001"
hidden_size <- "128"
model_type <- paste0(model_type, "_bs_", batch_size, "_lr_", lr, "_hidden_size_", hidden_size)
rates <- c("_0", "_10", "_20", "_30", "_40", "_50", "_60", "_70", "_80", "_90", "_100")
user_num <- 5
user <-c(0:(user_num-1))
strategies <- c(0:6) # 7 tasks
print('strategies')
print(strategies)
print('strategies length')
print(length(strategies))
N <- 1
unique_act_id <- c(1:5)
print('unique_act_id')
print(unique_act_id)
set.seed(9746234)
for (act_id in seq_along(unique_act_id)){
for (u in seq_along(user)){
print('user')
print(u)
for (rate in rates) {
N <- 1
dat <- vector("list", length(strategies))
for (i in seq_along(strategies)) {
if (rate=="_0"){
# read data from csv
dat[[i]] <- read.csv(paste0("../prediction/single_act/task", strategies[[i]], "/", model_type, "/user", user[[u]], "/rate_10", "_act_", unique_act_id[act_id], "_pred", ".csv"))
} else{
dat[[i]] <- read.csv(paste0("../prediction/single_act/task", strategies[[i]], "/", model_type, "/user", user[[u]], "/rate", rate, "_act_", unique_act_id[act_id], "_pred", ".csv"))
}
# strategy assumed for prediction
dat[[i]]$assumed_strategy <- strategies[[i]]
dat[[i]]$index <- dat[[i]]$action_id # sample based on intention
dat[[i]]$id <- dat[[i]][,1] # sample based on intention
}
save_path <- paste0("result/", "N", N, "/", model_type, "_N", N, "_", "result","_user", user[[u]], "_rate_", rate, "_act_", unique_act_id[act_id], ".csv")
dat_act <- do.call(rbind, dat) %>%
mutate(index = as.numeric(as.factor(id))) %>%
rename(true_strategy = task_name) %>%
mutate(
true_strategy = factor(
true_strategy, levels = 0:6,
labels = strategies
),
q_type = case_when(
gt %in% c(3,4,5) ~ 0,
gt %in% c(1,2,3,4,5,6,7) ~ 1,
gt %in% c(1,2,3,4) ~ 2,
gt %in% c(1,4,5,6,7) ~ 3,
gt %in% c(1,2,3,6,7) ~ 4,
gt %in% c(2,3,4,5,6,7) ~ 5,
gt %in% c(1,2,3,4,5,6,7) ~ 6,
)
)
dat_obs <- dat_act %>% filter(assumed_strategy == strategies[[i]])
N <- nrow(dat_obs)
print(c("N: ", N))
print(c("dim dat_act: ", dim(dat_act)))
q <- dat_obs$gt
true_strategy <- dat_obs$true_strategy
K <- length(unique(dat_act$assumed_strategy))
I <- 7
P_q_S <- array(dim = c(N, I, K))
for (n in 1:N) {
print(n)
P_q_S[n, , ] <- dat_act %>%
filter(index == n) %>%
select(matches("^act[[:digit:]]+$")) %>%
as.matrix() %>%
t()
for (k in 1:K) {
# normalize probabilities
P_q_S[n, , k] <- P_q_S[n, , k] / sum(P_q_S[n, , k])
}
}
print(c("dim(P_q_S)", dim(P_q_S)))
# read stan model
mod <- cmdstan_model(paste0(getwd(),"/strategy_inference_model.stan"))
if (rate=="_0"){
sub <- integer(0)
} else {
sub <- which(true_strategy == 0) # "0"
}
#print(sub)
#print(length(sub))
if (length(sub) == 1){
temp <- P_q_S[sub, , ]
dim(temp) <- c(1, dim(temp))
sdata <- list(N = length(sub), K = K, I = I, q = q[sub], P_q_S = temp)
} else{
sdata <- list(N = length(sub), K = K, I = I, q = q[sub], P_q_S = P_q_S[sub, , ])
}
fit_0 <- mod$sample(data = sdata, refresh=0, output_dir=paste0(getwd(),'/temp'))
print(fit_0$summary(NULL, c("mean","sd")))
if (rate=="_0"){
sub <- integer(0)
} else {
sub <- which(true_strategy == 1)
}
#print(sub)
#print(length(sub))
if (length(sub) == 1){
temp <- P_q_S[sub, , ]
dim(temp) <- c(1, dim(temp))
sdata <- list(N = length(sub), K = K, I = I, q = q[sub], P_q_S = temp)
} else{
sdata <- list(N = length(sub), K = K, I = I, q = q[sub], P_q_S = P_q_S[sub, , ])
}
fit_1 <- mod$sample(data = sdata, refresh=0, output_dir=paste0(getwd(),'/temp'))
print(fit_1$summary(NULL, c("mean","sd")))
if (rate=="_0"){
sub <- integer(0)
} else {
sub <- which(true_strategy == 2)
}
#print(sub)
#print(length(sub))
if (length(sub) == 1){
temp <- P_q_S[sub, , ]
dim(temp) <- c(1, dim(temp))
sdata <- list(N = length(sub), K = K, I = I, q = q[sub], P_q_S = temp)
} else{
sdata <- list(N = length(sub), K = K, I = I, q = q[sub], P_q_S = P_q_S[sub, , ])
}
fit_2 <- mod$sample(data = sdata, refresh=0, output_dir=paste0(getwd(),'/temp'))
print(fit_2$summary(NULL, c("mean","sd")))
if (rate=="_0"){
sub <- integer(0)
} else {
sub <- which(true_strategy == 3)
}
#print(sub)
#print(length(sub))
if (length(sub) == 1){
temp <- P_q_S[sub, , ]
dim(temp) <- c(1, dim(temp))
sdata <- list(N = length(sub), K = K, I = I, q = q[sub], P_q_S = temp)
} else{
sdata <- list(N = length(sub), K = K, I = I, q = q[sub], P_q_S = P_q_S[sub, , ])
}
fit_3 <- mod$sample(data = sdata, refresh=0, output_dir=paste0(getwd(),'/temp'))
print(fit_3$summary(NULL, c("mean","sd")))
if (rate=="_0"){
sub <- integer(0)
} else {
sub <- which(true_strategy == 4)
}
#print(sub)
#print(length(sub))
if (length(sub) == 1){
temp <- P_q_S[sub, , ]
dim(temp) <- c(1, dim(temp))
sdata <- list(N = length(sub), K = K, I = I, q = q[sub], P_q_S = temp)
} else{
sdata <- list(N = length(sub), K = K, I = I, q = q[sub], P_q_S = P_q_S[sub, , ])
}
fit_4 <- mod$sample(data = sdata, refresh=0, output_dir=paste0(getwd(),'/temp'))
print(fit_4$summary(NULL, c("mean","sd")))
if (rate=="_0"){
sub <- integer(0)
} else {
sub <- which(true_strategy == 5)
}
#print(sub)
#print(length(sub))
if (length(sub) == 1){
temp <- P_q_S[sub, , ]
dim(temp) <- c(1, dim(temp))
sdata <- list(N = length(sub), K = K, I = I, q = q[sub], P_q_S = temp)
} else{
sdata <- list(N = length(sub), K = K, I = I, q = q[sub], P_q_S = P_q_S[sub, , ])
}
fit_5 <- mod$sample(data = sdata, refresh=0, output_dir=paste0(getwd(),'/temp'))
print(fit_5$summary(NULL, c("mean","sd")))
if (rate=="_0"){
sub <- integer(0)
} else {
sub <- which(true_strategy == 6)
}
#print(sub)
#print(length(sub))
if (length(sub) == 1){
temp <- P_q_S[sub, , ]
dim(temp) <- c(1, dim(temp))
sdata <- list(N = length(sub), K = K, I = I, q = q[sub], P_q_S = temp)
} else{
sdata <- list(N = length(sub), K = K, I = I, q = q[sub], P_q_S = P_q_S[sub, , ])
}
fit_6 <- mod$sample(data = sdata, refresh=0, output_dir=paste0(getwd(),'/temp'))
print(fit_6$summary(NULL, c("mean","sd")))
# save csv
df <-rbind(fit_0$summary(), fit_1$summary(), fit_2$summary(), fit_3$summary(), fit_4$summary(), fit_5$summary(), fit_6$summary())
write.csv(df,file=save_path,quote=FALSE)
}
}
}

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library(tidyverse)
library(cmdstanr)
library(dplyr)
# index order of the strategies assumed throughout
model_type <- "lstmlast"
batch_size <- "8"
lr <- "0.0001"
hidden_size <- "128"
model_type <- paste0(model_type, "_bs_", batch_size, "_lr_", lr, "_hidden_size_", hidden_size)
rates <- c("_0", "_10", "_20", "_30", "_40", "_50", "_60", "_70", "_80", "_90", "_100")
user_num <- 5
user <-c(0:(user_num-1))
strategies <- c(0:6) # 7 tasks
print(strategies)
print(length(strategies))
N <- 1
set.seed(9736754)
#read data from csv
sel <- vector("list", length(strategies))
for (u in seq_along(user)){
print('user')
print(u)
for (rate in rates) {
dat <- vector("list", length(strategies))
for (i in seq_along(strategies)) {
if (rate=="_0"){
dat[[i]] <- read.csv(paste0("../prediction/task", strategies[[i]], "/", model_type, "/user", user[[u]], "_rate_10", "_pred", ".csv"))
} else if (rate=="_100"){
dat[[i]] <- read.csv(paste0("../prediction/task", strategies[[i]], "/", model_type, "/user", user[[u]], "_pred", ".csv"))
} else{
dat[[i]] <- read.csv(paste0("../prediction/task", strategies[[i]], "/", model_type, "/user", user[[u]], "_rate", rate, "_pred", ".csv"))
}
# strategy assumed for prediction
dat[[i]]$assumed_strategy <- strategies[[i]]
dat[[i]]$index <- dat[[i]]$action_id
dat[[i]]$id <- dat[[i]][,1]
}
# reset N after inference
N <- 1
# select all action series and infer every one
if (rate == "_0"){
sel[[1]]<-dat[[1]] %>%
group_by(task_name) %>%
sample_n(N)
sel[[1]] <- data.frame(sel[[1]])
unique_act_id <- unique(sel[[1]]$action_id)
}
print(sel[[1]]$action_id)
print(sel[[1]]$task_name)
print(dat[[1]]$task_name)
for (i in seq_along(strategies)) {
dat[[i]]<-subset(dat[[i]], dat[[i]]$action_id == sel[[1]]$action_id[1])
}
row.names(dat) <- NULL
print(c('action id', dat[[1]]$action_id))
print(c('action id', dat[[2]]$action_id))
print(c('action id', dat[[3]]$action_id))
dir.create(file.path(paste0("result/", "N", N)), showWarnings = FALSE)
save_path <- paste0("result/", "N", N, "/", model_type, "_N", N, "_", "result","_user", user[[u]], "_rate_", rate, ".csv")
dat_act <- do.call(rbind, dat) %>%
mutate(index = as.numeric(as.factor(id))) %>%
rename(true_strategy = task_name) %>%
mutate(
true_strategy = factor(
true_strategy, levels = 0:6,
labels = strategies
),
q_type = case_when(
gt %in% c(3,4,5) ~ 0,
gt %in% c(1,2,3,4,5,6,7) ~ 1,
gt %in% c(1,2,3,4) ~ 2,
gt %in% c(1,4,5,6,7) ~ 3,
gt %in% c(1,2,3,6,7) ~ 4,
gt %in% c(2,3,4,5,6,7) ~ 5,
gt %in% c(1,2,3,4,5,6,7) ~ 6,
)
)
dat_obs <- dat_act %>% filter(assumed_strategy == strategies[[i]]) # put_fridge, was num
N <- nrow(dat_obs)
print(c("N: ", N))
print(c("dim dat_act: ", dim(dat_act)))
q <- dat_obs$gt
true_strategy <- dat_obs$true_strategy
K <- length(unique(dat_act$assumed_strategy))
I <- 7
P_q_S <- array(dim = c(N, I, K))
for (n in 1:N) {
print(n)
P_q_S[n, , ] <- dat_act %>%
filter(index == n) %>%
select(matches("^act[[:digit:]]+$")) %>%
as.matrix() %>%
t()
for (k in 1:K) {
# normalize probabilities
P_q_S[n, , k] <- P_q_S[n, , k] / sum(P_q_S[n, , k])
}
}
print(c("dim(P_q_S)", dim(P_q_S)))
mod <- cmdstan_model(paste0(getwd(),"/strategy_inference_model.stan"))
if (rate=="_0"){
sub <- integer(0)
} else {
sub <- which(true_strategy == 0) # "0"
}
if (length(sub) == 1){
temp <- P_q_S[sub, , ]
dim(temp) <- c(1, dim(temp))
sdata <- list(N = length(sub), K = K, I = I, q = q[sub], P_q_S = temp)
} else{
sdata <- list(N = length(sub), K = K, I = I, q = q[sub], P_q_S = P_q_S[sub, , ])
}
fit_0 <- mod$sample(data = sdata, refresh=0, output_dir=paste0(getwd(),"/temp"))
print(fit_0$summary(NULL, c("mean","sd")))
if (rate=="_0"){
sub <- integer(0)
} else {
sub <- which(true_strategy == 1)
}
if (length(sub) == 1){
temp <- P_q_S[sub, , ]
dim(temp) <- c(1, dim(temp))
sdata <- list(N = length(sub), K = K, I = I, q = q[sub], P_q_S = temp)
} else{
sdata <- list(N = length(sub), K = K, I = I, q = q[sub], P_q_S = P_q_S[sub, , ])
}
fit_1 <- mod$sample(data = sdata, refresh=0, output_dir=paste0(getwd(),"/temp"))
print(fit_1$summary(NULL, c("mean","sd")))
if (rate=="_0"){
sub <- integer(0)
} else {
sub <- which(true_strategy == 2)
}
if (length(sub) == 1){
temp <- P_q_S[sub, , ]
dim(temp) <- c(1, dim(temp))
sdata <- list(N = length(sub), K = K, I = I, q = q[sub], P_q_S = temp)
} else{
sdata <- list(N = length(sub), K = K, I = I, q = q[sub], P_q_S = P_q_S[sub, , ])
}
fit_2 <- mod$sample(data = sdata, refresh=0, output_dir=paste0(getwd(),"/temp"))
print(fit_2$summary(NULL, c("mean","sd")))
if (rate=="_0"){
sub <- integer(0)
} else {
sub <- which(true_strategy == 3)
}
if (length(sub) == 1){
temp <- P_q_S[sub, , ]
dim(temp) <- c(1, dim(temp))
sdata <- list(N = length(sub), K = K, I = I, q = q[sub], P_q_S = temp)
} else{
sdata <- list(N = length(sub), K = K, I = I, q = q[sub], P_q_S = P_q_S[sub, , ])
}
fit_3 <- mod$sample(data = sdata, refresh=0, output_dir=paste0(getwd(),"/temp"))
print(fit_3$summary(NULL, c("mean","sd")))
if (rate=="_0"){
sub <- integer(0)
} else {
sub <- which(true_strategy == 4)
}
if (length(sub) == 1){
temp <- P_q_S[sub, , ]
dim(temp) <- c(1, dim(temp))
sdata <- list(N = length(sub), K = K, I = I, q = q[sub], P_q_S = temp)
} else{
sdata <- list(N = length(sub), K = K, I = I, q = q[sub], P_q_S = P_q_S[sub, , ])
}
fit_4 <- mod$sample(data = sdata, refresh=0, output_dir=paste0(getwd(),"/temp"))
print(fit_4$summary(NULL, c("mean","sd")))
if (rate=="_0"){
sub <- integer(0)
} else {
sub <- which(true_strategy == 5)
}
if (length(sub) == 1){
temp <- P_q_S[sub, , ]
dim(temp) <- c(1, dim(temp))
sdata <- list(N = length(sub), K = K, I = I, q = q[sub], P_q_S = temp)
} else{
sdata <- list(N = length(sub), K = K, I = I, q = q[sub], P_q_S = P_q_S[sub, , ])
}
fit_5 <- mod$sample(data = sdata, refresh=0, output_dir=paste0(getwd(),"/temp"))
print(fit_5$summary(NULL, c("mean","sd")))
if (rate=="_0"){
sub <- integer(0)
} else {
sub <- which(true_strategy == 6)
}
if (length(sub) == 1){
temp <- P_q_S[sub, , ]
dim(temp) <- c(1, dim(temp))
sdata <- list(N = length(sub), K = K, I = I, q = q[sub], P_q_S = temp)
} else{
sdata <- list(N = length(sub), K = K, I = I, q = q[sub], P_q_S = P_q_S[sub, , ])
}
fit_6 <- mod$sample(data = sdata, refresh=0, output_dir=paste0(getwd(),"/temp"))
print(fit_6$summary(NULL, c("mean","sd")))
# save csv
df <-rbind(fit_0$summary(), fit_1$summary(), fit_2$summary(), fit_3$summary(), fit_4$summary(), fit_5$summary(), fit_6$summary())
write.csv(df,file=save_path,quote=FALSE)
}
}