...
RsiMa = ema(Rsi, SF)
AtrRsi = abs(RsiMa[1] - RsiMa)
MaAtrRsi = ema(AtrRsi, Wilders_Period)
dar = ema(MaAtrRsi, Wilders_Period) * QQE
longband = 0.0
shortband = 0.0
DeltaFastAtrRsi = dar
RSIndex = RsiMa
newshortband = RSIndex + DeltaFastAtrRsi
newlongband = RSIndex - DeltaFastAtrRsi
// what's going on here??
longband := RSIndex[1] > longband[1] and RSIndex > longband[1] ? max(longband[1], newlongband) : newlongband
shortband := RSIndex[1] < shortband[1] and RSIndex < shortband[1] ? min(shortband[1], newshortband) : newshortband
...
Hi - I'm trying to understand what longband (and shortband) are being set to. It's very confusing because longband is initialized to a single value 0.0 but newlongband appears to be a time series. I can't make out what's going on here. I'm assuming that the RSIndex (or any time series) in the longband assignment expression is really RSIndex[0] (just like in close). After studying the manual, it doesn't look like to me that the min function or the > comparator can apply to a time series although I could be wrong. I'm also assuming that longband[1] refers to a single value - not another time series beginning at 1 instead of 0.
Very grateful for any thoughts.
Related
Py newbie here (really though, I literally started yesterday)
Im basically trying to do a coin flip simulation, and its based on a BIASED distribution!
Following are the inputs needed for the sim
#prob_up (success) = 0.472835862
#prob_down(fail) = 0.527164138
#up_scale(rate of increase) = 0.091889211
#down_scale(rate of decrease) = -0.061319729
#value = 1
Below is what I did
```for i in range(30):
toss_outcome = np.random.random()
if toss_outcome <= prob_up:
value += value*(up_scale)
if toss_outcome > 1-prob_up:
value += value*(down_scale)
print(value)```
So this one works for me, but I would like to ask you how I could repeat this simulation for 100 times under one loop so that I can get something like 1.1, 1.09, 2.1, 0.98, 1.7, 0.89...
This is what I tried
#value =1
#count = 0
```for i in range(10):
count = 0
sims = []
for x in range(20):
toss_outcome = np.random.random()
if toss_outcome <= prob_up:
value+= value*(up_scale)
if toss_outcome > 1-prob_down:
value+= value*(down_scale)
if count == 20:
break
sims.append(value)
print(sims) ```
This is what I got
1.0918892105282665, 1.192222048068041, 1.3017743908394064, 1.2219499374001699, 1.1470202978485873, 1.0766853235214044, 1.1756210878871576, 1.1035323208530166, ...1.5467124250543491, 1.4518684376272906, 1.362840257848346, 1.4880705732181698 #Series 1
1.6248082034015325, 1.7741105464719504, 1.9371321639771295, 1.8183477437909779, 1.7068471521124233, 1.602183746548087, 1.5039382726953088, 1.4117171847179661, 1.541438762310887... #Series 2
So what happened here is that the new series simply continued from the previous one, instead of starting a new series of simulation from value = 1...
Id sincerely appreciate your help! Have a wonderful day.
For the main.py of the px2graph project, the part of training and validation is shown as below:
splits = [s for s in ['train', 'valid'] if opt.iters[s] > 0]
start_round = opt.last_round - opt.num_rounds
# Main training loop
for round_idx in range(start_round, opt.last_round):
for split in splits:
print("Round %d: %s" % (round_idx, split))
loader.start_epoch(sess, split, train_flag, opt.iters[split] * opt.batchsize)
flag_val = split == 'train'
for step in tqdm(range(opt.iters[split]), ascii=True):
global_step = step + round_idx * opt.iters[split]
to_run = [sample_idx, summaries[split], loss, accuracy]
if split == 'train': to_run += [optim]
# Do image summaries at the end of each round
do_image_summary = step == opt.iters[split] - 1
if do_image_summary: to_run[1] = image_summaries[split]
# Start with lower learning rate to prevent early divergence
t = 1/(1+np.exp(-(global_step-5000)/1000))
lr_start = opt.learning_rate / 15
lr_end = opt.learning_rate
tmp_lr = (1-t) * lr_start + t * lr_end
# Run computation graph
result = sess.run(to_run, feed_dict={train_flag:flag_val, lr:tmp_lr})
out_loss = result[2]
out_accuracy = result[3]
if sum(out_loss) > 1e5:
print("Loss diverging...exiting before code freezes due to NaN values.")
print("If this continues you may need to try a lower learning rate, a")
print("different optimizer, or a larger batch size.")
return
time_str = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
print("{}: step {}, loss {:g}, acc {:g}".format(time_str, global_step, out_loss, out_accuracy))
# Log data
if split == 'valid' or (split == 'train' and step % 20 == 0) or do_image_summary:
writer.add_summary(result[1], global_step)
writer.flush()
# Save training snapshot
saver.save(sess, 'exp/' + opt.exp_id + '/snapshot')
with open('exp/' + opt.exp_id + '/last_round', 'w') as f:
f.write('%d\n' % round_idx)
It seems that the author only get the result of each batch of the validation set. I am wondering, if I want to observe whether the model is improving or reaching the best performance, should I use the result on the whole validation set?
If the validation set is small enough, we could calculate the loss, accuracy on the whole validation set during training to observe the performance. However, if the validation set is too large, it is better to calculate batch-wise validation results and for multiple steps.
I have to deal with very big data (Point clouds generally more than 30 000 000 points) using Matlab. I can read ascii data using textscan function. After reading, I need to detect invalid data (points with 0,0,0 coordinates) and then I need to do some mathematical operations on each point or each line in the data. In my way, first I read data with textscan and then I assign this data to a matrix. Secondly, I use for loops for detecting invalid points and doing some mathematical operations on each point or line in the data. A sample of my code is shown as below. According to profile tool of Matlab textscan takes 37% and line
transformed_list((i:i),(1:4)) = coordinate_list((i:i),(1:4))*t_matrix;
takes 35% of all computation time.
I tried it with another point cloud (stores around 5 500 000) and profile tool reported same results. Is there a way of avoiding for loops, or is there another way of speeding up this computation?
fileID = fopen('C:\Users\Mustafa\Desktop\ptx_all_data\dede5.ptx');
original_data = textscan(fileID,'%f %f %f %f %f %f %f', 'delimiter',' ');
fclose(fileID);
column = original_data{1}(1);
row = original_data{1}(2);
t_matrix = [original_data{1}(7) original_data{2}(7) original_data{3}(7) original_data{4}(7)
original_data{1}(8) original_data{2}(8) original_data{3}(8) original_data{4}(8)
original_data{1}(9) original_data{2}(9) original_data{3}(9) original_data{4}(9)
original_data{1}(10) original_data{2}(10) original_data{3}(10) original_data{4}(10)];
coordinate_list(:,1) = original_data{1}(11:length(original_data{1}));
coordinate_list(:,2) = original_data{2}(11:length(original_data{2}));
coordinate_list(:,3) = original_data{3}(11:length(original_data{3}));
coordinate_list(:,4) = 0;
coordinate_list(:,5) = original_data{4}(11:length(original_data{4}));
transformed_list = zeros(length(coordinate_list),5);
for i = 1:length(coordinate_list)
if coordinate_list(i,1) == 0 && coordinate_list(i,2) == 0 && coordinate_list(i,3) == 0
transformed_list(i,:) = NaN;
else
%transformed_list(i,:) = coordinate_list(i,:)*t_matrix;
transformed_list((i:i),(1:4)) = coordinate_list((i:i),(1:4))*t_matrix;
transformed_list(i,5) = coordinate_list(i,5);
end
%i
end
Thanks in advance
for loops with conditional statements like those will take ages to run. But what Matlab lacks in loop speed it makes up with vectorization and indexing.
Let's try some logical indexing like this to solve the first step:
coordinate_list(coordinate_list(:,1) == 0 .* ...
coordinate_list(:,2) == 0 .* ...
coordinate_list(:,3) == 0)=nan;
And then vectorize the second statement:
transformed_list(:,(1:4)) = coordinate_list(:,(1:4))*t_matrix;
As EBH mentioned above this might be a bit heavy on your RAM. If it's more than your computer can handle asks yourself if the coordinates really have to be doubles, maybe single precision will do. If that still doesn't do, try slicing the vector and performing the operation in parts.
Small example to give you an idea because I had a 2million element point cloud around here:
In R2015a
transformed_list = zeros(length(coordinate_list),5);
tic
for i = 1:length(coordinate_list)
if coordinate_list(i,1) == 0 && coordinate_list(i,2) == 0 && coordinate_list(i,3) == 0
transformed_list(i,:) = NaN;
else
%transformed_list(i,:) = coordinate_list(i,:)*t_matrix;
transformed_list((i:i),(1:3)) = coordinate_list((i:i),(1:3))*t_matrix;
transformed_list(i,5) = 1;
end
%i
end
toc
Returns Elapsed time is 10.928142 seconds.
transformed_list=coordinate_list;
tic
coordinate_list(coordinate_list(:,1) == 0 .* ...
coordinate_list(:,2) == 0 .* ...
coordinate_list(:,3) == 0)=nan;
transformed_list(:,(1:3)) = coordinate_list(:,(1:3))*t_matrix;
toc
Returns Elapsed time is 0.101696 seconds.
Rather than read the whole file, you'd be better off using a loop with
fscanf(fileID, '%f', 7)
and processing input as you read it.
I'm currently developping a VOIP tool in python working as a client-server. My problem is that i'm currently sending the Pyaudio input stream as follows even when there is no sound (well, when nobody talks or there is no noise, data is sent as well) :
CHUNK = 1024
p = pyaudio.PyAudio()
stream = p.open(format = pyaudio.paInt16,
channels = 1,
rate = 44100,
input = True,
frames_per_buffer = CHUNK)
while 1:
self.conn.sendVoice(stream.read(CHUNK))
I would like to check volume to get something like this :
data = stream.read(CHUNK)
if data.volume > 20%:
self.conn.sendVoice(data)
This way I could avoid sending useless data and spare connection/ increase performance. (Also, I'm looking for some kind of compression but I think I will have to ask it in another topic).
Its can be done using root mean square (RMS).
One way to build your own rms function using python is:
def rms( data ):
count = len(data)/2
format = "%dh"%(count)
shorts = struct.unpack( format, data )
sum_squares = 0.0
for sample in shorts:
n = sample * (1.0/32768)
sum_squares += n*n
return math.sqrt( sum_squares / count )
Another choice is use audioop to find rms:
data = stream.read(CHUNK)
rms = audioop.rms(data,2)
Now if do you want you can convert rms to decibel scale decibel = 20 * log10(rms)
I'm trying to implement a PID controller following http://en.wikipedia.org/wiki/PID_controller
The mechanism I try to control works as follows:
1. I have an input variable which I can control. Typical values would be 0.5...10.
2. I have an output value which I measure daily. My goal for the output is roughly at the same range.
The two variables have strong correlation - when the process parameter goes up, the output generally goes up, but there's quite a bit of noise.
I'm following the implementation here:
http://code.activestate.com/recipes/577231-discrete-pid-controller/
Now the PID seems like it is correlated with the error term, not the measured level of output. So my guess is that I am not supposed to use it as-is for the process variable, but rather as some correction to the current value? How is that supposed to work exactly?
For example, if we take Kp=1, Ki=Kd=0, The process (input) variable is 4, the current output level is 3 and my target is a value of 2, I get the following:
error = 2-3 = -1
PID = -1
Then I should set the process variable to -1? or 4-1=3?
You need to think in terms of the PID controller correcting a manipulated variable (MV) for errors, and that you need to use an I term to get to an on-target steady-state result. The I term is how the PID retains and applies memory of the prior behavior of the system.
If you are thinking in terms of the output of the controller being changes in the MV, it is more of a 'velocity form' PID, and the memory of prior errors and behavior is integrated and accumulated in the prior MV setting.
From your example, it seems like a manipulated value of -1 is not feasible and that you would like the controller to suggest a value like 3 to get a process output (PV) of 2. For a PID controller to make use of "The process (input) variable is 4,..." (MV in my terms) Ki must be non-zero, and if the system was at steady-state, whatever was accumulated in the integral (sum_e=sum(e)) would precisely equal 4/Ki, so:
Kp= Ki = 1 ; Kd =0
error = SV - PV = 2 - 3 = -1
sum_e = sum_e + error = 4/Ki -1
MV = PID = -1(Kp) + (4/Ki -1)Ki = -1Kp + 4 - 1*Ki = -1 +4 -1 = 2
If you used a slower Ki than 1, it would smooth out the noise more and not adjust the MV so quickly:
Ki = 0.1 ;
MV = PID = -1(Kp) + (4/Ki -1)Ki = -1Kp + 4 - 1*Ki = -1 +4 -0.1 = 2.9
At steady state at target (PV = SV), sum_e * Ki should produce the steady-state MV:
PV = SV
error = SV - PV = 0
Kp * error = 0
MV = 3 = PID = 0 * Kp + Ki * sum_e
A nice way to understand the PID controller is to put units on everything and think of Kp, Ki, Kd as conversions of the process error, accumulated error*timeUnit, and rate-of-change of error/timeUnit into terms of the manipulated variable, and that the controlled system converts the controller's manipulated variable into units of output.