def show_bias(self):
if len(self.weights) is 1:
mp = MyPlot()
mp.set_labels('Step', 'Bias')
mp.show_list(self.biases)
else:
print('Cannot show the bias! Call print_bias mehtod.')
def show_weight(self):
print('shape=', self.weights)
if len(self.weights[0]) is 1:
mp = MyPlot()
mp.set_labels('Step', 'Weight')
mp.show_list(self.weights)
else:
print('Cannot show the weight! Call print_weight method.')
def __init__(self, *args, **kwargs):
super(Wnd, self).__init__(*args, **kwargs)
self.t = None
self.osc = None
self.input_cbox = None
self.output_cbox = None
left = VContainer()
right = VContainer()
self.slider = QtWidgets.QSlider(Qt.Horizontal)
self.slider.setMinimum(20)
self.slider.setMaximum(500)
left.addWidget(self.slider)
self.slider.valueChanged.connect(self.change_freq)
self.addWidget(left)
self.addWidget(right)
devices = HContainer()
devices.addWidget(self.inputCBox())
devices.addWidget(self.outputCBox())
left.addWidget(devices)
spf = wave.open("wav.wav", "r")
signal = spf.readframes(-1)
signal = np.frombuffer(signal, "int16")
self.signal = signal
self.graphWidget = MyPlot()
left.addWidget(self.graphWidget)
self.fft = Spec()
# self.fft.setYRange(0, 100000, padding=0)
left.addWidget(self.fft)
self.buttons = HContainer()
btn = QtWidgets.QPushButton()
btn.setText('Record')
self.buttons.addWidget(btn)
btn.clicked.connect(self.get_device_num)
btn = QtWidgets.QPushButton()
btn.setText('Play')
self.buttons.addWidget(btn)
btn.clicked.connect(self.play_file)
left.addWidget(self.buttons)
self.logger = LogWrite()
right.addWidget(self.logger)
#----- a neuron
w = tf.Variable(tf.random_normal([2, 1]))
b = tf.Variable(tf.random_normal([1]))
hypo = tf.matmul(x, w) + b
#-----
cost = tf.reduce_mean((hypo - y) * (hypo - y))
train = tf.train.GradientDescentOptimizer(learning_rate=0.01).minimize(cost)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
costs = []
for i in range(2001):
sess.run(train)
if i % 50 == 0:
print('hypo:', sess.run(hypo), '|', sess.run(w), sess.run(b),
sess.run(cost))
costs.append(sess.run(cost))
hypo2 = tf.matmul([[4., 4]], w) + b
print(sess.run(hypo2))
p = MyPlot()
p.show_list(costs)
def show_error(self):
mp = MyPlot()
mp.set_labels('Step', 'Error')
mp.show_list(self.costs)
class LineParser(object):
__rules = [
'CREATEVAR',
'SETVAR',
'CALCULATE',
'CREATEPLOT',
'ADDTOPLOT',
'SHOWPLOT',
'CREATEFCT',
'ENDFCT',
'CALL']
__function_create_start = None
__variables = Variable()
__plots = MyPlot()
__functions = Function()
def __init__(self, line):
self.line = line
self.error = 0
self.rule_in_line = ''
def update_globals(self, globals):
pass
def get_rule(self):
for __rule in LineParser.__rules:
__location = -1
__matching_rules = 0
while __matching_rules <=2: #search multiple identical rules in the same line
__location = self.line.find(__rule, __location + 1)
if __location == -1:
break
else:
__matching_rules += 1
if __matching_rules == 1:
self.rule_in_line = __rule
self.error += 1
else:
self.error += 1
def implement_rule(self):
if LineParser.__function_create_start == None:
if self.rule_in_line == 'CREATEVAR':
LineParser.__variables.CreateVar((self.line.split(':')[1]).split('\n')[0])
MyPlot.UpdateGlobals(Variable.variables)
elif self.rule_in_line == 'SETVAR':
LineParser.__variables.SetVar(self.line.split(':')[1], (self.line.split(':')[2]).split('\n')[0])
MyPlot.UpdateGlobals(Variable.variables)
elif self.rule_in_line == 'CALCULATE':
LineParser.__variables.Calculate(self.line.split(':')[1], (self.line.split(':')[2]).split('\n')[0])
MyPlot.UpdateGlobals(Variable.variables)
elif self.rule_in_line == 'CREATEPLOT':
LineParser.__plots.CreatePlot((self.line.split(':')[1]).split('\n')[0])
elif self.rule_in_line == 'ADDTOPLOT':
LineParser.__plots.AddToPlot(self.line.split(':')[1], self.line.split(':')[2], (self.line.split(':')[3]).split('\n')[0])
elif self.rule_in_line == 'SHOWPLOT':
LineParser.__plots.ShowPlot((self.line.split(':')[1]).split('\n')[0])
elif self.rule_in_line == 'CREATEFCT':
LineParser.__function_create_start = (self.line.split(':')[1]).split('\n')[0]
LineParser.__functions.CreateFunction((self.line.split(':')[1]).split('\n')[0])
elif self.rule_in_line == 'CALL':
LineParser.__functions.CallFunction((self.line.split(':')[1]).split('\n')[0])
else:
pass
else:
if self.rule_in_line == 'CREATEVAR':
LineParser.__functions.AddToFunction(LineParser.__function_create_start, self.line)
elif self.rule_in_line == 'SETVAR':
LineParser.__functions.AddToFunction(LineParser.__function_create_start, self.line)
elif self.rule_in_line == 'CALCULATE':
LineParser.__functions.AddToFunction(LineParser.__function_create_start, self.line)
elif self.rule_in_line == 'CREATEPLOT':
LineParser.__functions.AddToFunction(LineParser.__function_create_start, self.line)
elif self.rule_in_line == 'ADDTOPLOT':
LineParser.__functions = Function()
LineParser.__functions.AddToFunction(LineParser.__function_create_start, self.line)
elif self.rule_in_line == 'SHOWPLOT':
LineParser.__functions.AddToFunction(LineParser.__function_create_start, self.line)
elif self.rule_in_line == 'ENDFCT':
LineParser.__function_create_start = None
from myplot import MyPlot
import sys
data = [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]
print(data)
print('Number of arguments:', len(sys.argv), 'arguments.')
print('Argument List:', str(sys.argv))
#type_of_graph = int(input("What is the type of graph you want. \n Press 1 for histogram \n Press 2 for bar chart \n Press 3 for scatter plot. \n Press 4 for line plot \n Press 5 for histogram\n Press 6 for pie chart"))
#{'b', 'g', 'r', 'c', 'm', 'y', 'k', 'w'}
type_of_graph = int(sys.argv[1])
myplt = MyPlot(type_of_graph, str(sys.argv[2]), data)
myplt.plot_graph()
#fig, axs = plt.subplots(2, 2, figsize=(5, 5))
#axs[0, 0].hist(data[0])
#axs[1, 0].scatter(data[0], data[1])
#axs[0, 1].plot(data[0], data[1])
#axs[1, 1].hist2d(data[0], data[1])
self.addPoint(state.PosAct)
# A worker class for the ros node
# To be deprecated soon
class WorkerListener(Thread):
def __init__(self):
Thread.__init__(self)
def run(self):
rospy.ready(NAME, anonymous=True)
rospy.spin()
channel = MyChannel()
channel2 = MyChannelAct()
rospy.TopicSub('ARM_L_PAN_state', RotaryJointState, channel.callback)
rospy.TopicSub('ARM_L_PAN_state', RotaryJointState, channel2.callback)
app = wx.PySimpleApp(0)
worker = WorkerListener()
worker.start()
frame = wx.Frame(None, -1, "")
panel = MyPlot(frame)
panel.addChannel(channel)
panel.addChannel(channel2)
frame.Show()
print '>>>>>>>>>>>>>>'
app.MainLoop()
print '>>>>>>>>>>>>>>'
#rospy.spin()
from myplot import MyPlot
x_data = [1]
y_data = [1]
#----- a neuron
w = tf.Variable(tf.random_normal([1]))
b = tf.Variable(tf.random_normal([1]))
hypo = w * x_data + b
#-----
cost = (hypo - y_data) ** 2
train = tf.train.GradientDescentOptimizer(learning_rate=0.01).minimize(cost)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
costs = []
for i in range(1001):
sess.run(train)
if i % 50 == 0:
print(sess.run(w), sess.run(b), sess.run(cost))
costs.append(sess.run(cost))
gildong = MyPlot()
gildong.show_list(costs)