cat /proc/net/sctp/assocs
ASSOC-ID ASSOC SOCK STY SST ST HBKT TX_QUEUE RX_QUEUE UID INODE LPORT RPORT LADDRS <-> RADDRS HBINT INS OUTS MAXRT T1X T2X RTXC wmema wmemq sndbuf rcvbuf
13 ffff8800b93a9000 ffff8800ac752300 2 1 3 9176 0 0 0 20735 3905 48538 *192.168.44.228 <-> *A172.16.236.92 7500 10 10 2 0 0 23 1 0 2000000 2000000
0 ffff88042aea3000 ffff880422e88000 0 10 1 40840 0 0 0 9542 3868 3868 *10.127.58.66 <-> *U10.127.115.194 7500 17 17 10 0 0 0 1 0 8388608 8388608
I need to record sounds from the iPhone mic and process the samples in my Swift app. I would really appreciate an example to follow.
These Recording settings
let recordSettings:[String : AnyObject] = [
AVFormatIDKey: NSNumber(unsignedInt:kAudioFormatLinearPCM),
//AVEncoderAudioQualityKey : AVAudioQuality.Max.rawValue,
// AVEncoderBitRateKey : 320000,
AVNumberOfChannelsKey: 1,
AVSampleRateKey : 8000.0,
AVLinearPCMBitDepthKey: 16,
AVLinearPCMIsBigEndianKey: "true",
AVLinearPCMIsFloatKey: "false"
]
produce the following bytes
99 97 102 102 0 1 0 0 100 101 115 99 0 0 0 0 0 0 0 32 64 191 64 0 0 0 0 0 108 112 99 109 0 0 0 2 0 0 0 2 0 0 0 1 0 0 0 1 0 0 0 16 102 114 101 101 0 0 0 0 0 0 15 176 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Which looks fishy maybe it's a double still?
I already created an example for something like that. It gets the data from the microphone, does a Fast-Fourier-Transform on it to find the frequencies and displays them in the view. It's done for OSX, but it should also work on iOS:
https://github.com/Kametrixom/Frequencies
My Erlang applicaation processed crashed and then exited, but found that the erlang VM is still running.
I could recieve pong when ping this "suspended node"
Types regs() and the results show below, there is not my app process.
(hub#192.168.1.140)4> regs().
** Registered procs on node 'hub#192.168.1.140' **
Name Pid Initial Call Reds Msgs
application_controlle <0.7.0> erlang:apply/2 30258442 1390
auth <0.20.0> auth:init/1 189 0
code_server <0.26.0> erlang:apply/2 1194028 0
erl_epmd <0.19.0> erl_epmd:init/1 138 0
erl_prim_loader <0.3.0> erlang:apply/2 2914236 0
error_logger <0.6.0> gen_event:init_it/6 49983527 0
file_server_2 <0.25.0> file_server:init/1 16185407 0
global_group <0.24.0> global_group:init/1 107 0
global_name_server <0.13.0> global:init/1 1385 0
gr_counter_sup <0.43.0> supervisor:gr_counter_sup 253 0
gr_lager_default_trac <0.70.0> gr_counter:init/1 121 0
gr_lager_default_trac <0.72.0> gr_manager:init/1 46 0
gr_lager_default_trac <0.69.0> gr_param:init/1 117 0
gr_lager_default_trac <0.71.0> gr_manager:init/1 46 0
gr_manager_sup <0.45.0> supervisor:gr_manager_sup 484 0
gr_param_sup <0.44.0> supervisor:gr_param_sup/1 253 0
gr_sup <0.42.0> supervisor:gr_sup/1 237 0
inet_db <0.16.0> inet_db:init/1 749 0
inet_gethost_native <0.176.0> inet_gethost_native:serve 4698517 0
inet_gethost_native_s <0.175.0> supervisor_bridge:inet_ge 41 0
init <0.0.0> otp_ring0:start/2 30799457 0
kernel_safe_sup <0.35.0> supervisor:kernel/1 278 0
kernel_sup <0.11.0> supervisor:kernel/1 47618 0
lager_crash_log <0.52.0> lager_crash_log:init/1 97712230 0
lager_event <0.50.0> gen_event:init_it/6 1813660437 0
lager_handler_watcher <0.51.0> supervisor:lager_handler_ 358 0
lager_sup <0.49.0> supervisor:lager_sup/1 327 0
net_kernel <0.21.0> net_kernel:init/1 110769667 0
net_sup <0.18.0> supervisor:erl_distributi 313 0
os_cmd_port_creator <0.582.0> erlang:apply/2 81 0
rex <0.12.0> rpc:init/1 15653480 0
standard_error <0.28.0> erlang:apply/2 9 0
standard_error_sup <0.27.0> supervisor_bridge:standar 41 0
timer_server <0.100.0> timer:init/1 59356077 0
user <0.31.0> group:server/3 23837008 0
user_drv <0.30.0> user_drv:server/2 12239455 0
** Registered ports on node 'hub#192.168.1.140' **
Name Id Command
ok
It rarely occurs, but anyone explains it?
System: CentOS 5.8
Erlang: R15B03
I'm getting started with Puppet. I added some lines to a manifest and when running Puppet now it's been at 100% cpu for a very longtime. Is there a good way to see what puppet is actually doing? puppettop?
top gives me this, which is quite useless:
top - 20:02:11 up 1 day, 2:30, 5 users, load average: 1.02, 1.12, 0.93
Tasks: 164 total, 2 running, 162 sleeping, 0 stopped, 0 zombie
Cpu(s): 12.5%us, 0.0%sy, 0.0%ni, 87.4%id, 0.0%wa, 0.0%hi, 0.0%si, 0.0%st
Mem: 32809072k total, 10412396k used, 22396676k free, 243832k buffers
Swap: 16768892k total, 0k used, 16768892k free, 6978500k cached
PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND
9761 root 20 0 646m 558m 3568 R 100 1.7 20:42.88 puppet
10509 guaka 20 0 17344 1352 972 R 0 0.0 0:00.28 top
1 root 20 0 24216 2192 1344 S 0 0.0 0:00.85 init
2 root 20 0 0 0 0 S 0 0.0 0:00.00 kthreadd
3 root 20 0 0 0 0 S 0 0.0 0:02.83 ksoftirqd/0
4 root 20 0 0 0 0 S 0 0.0 0:00.00 kworker/0:0
5 root 0 -20 0 0 0 S 0 0.0 0:00.00 kworker/0:0H
I tried to create a neural network to estimate y = x ^ 2. So I created a fitting neural network and gave it some samples for input and output. I tried to build this network in C++. But the result is different than I expected.
With the following inputs:
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49
50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 -1
-2 -3 -4 -5 -6 -7 -8 -9 -10 -11 -12 -13 -14 -15 -16 -17 -18 -19 -20 -21 -22 -23 -24 -25 -26 -27 -28 -29 -30 -31 -32 -33 -34 -35 -36 -37 -38 -39 -40 -41 -42 -43 -44 -45 -46 -47 -48 -49 -50 -51 -52 -53 -54 -55 -56 -57 -58 -59 -60 -61 -62 -63 -64 -65 -66 -67 -68 -69 -70 -71
and the following outputs:
0 1 4 9 16 25 36 49 64 81 100 121 144 169 196 225 256 289 324 361 400
441 484 529 576 625 676 729 784 841 900 961 1024 1089 1156 1225 1296
1369 1444 1521 1600 1681 1764 1849 1936 2025 2116 2209 2304 2401 2500
2601 2704 2809 2916 3025 3136 3249 3364 3481 3600 3721 3844 3969 4096
4225 4356 4489 4624 4761 4900 5041 1 4 9 16 25 36 49 64 81 100 121 144
169 196 225 256 289 324 361 400 441 484 529 576 625 676 729 784 841
900 961 1024 1089 1156 1225 1296 1369 1444 1521 1600 1681 1764 1849
1936 2025 2116 2209 2304 2401 2500 2601 2704 2809 2916 3025 3136 3249
3364 3481 3600 3721 3844 3969 4096 4225 4356 4489 4624 4761 4900 5041
I used fitting tool network. with matrix rows. Training is 70%, validation is 15% and testing is 15% as well. The number of hidden neurons is two. Then in command lines I wrote this:
purelin(net.LW{2}*tansig(net.IW{1}*inputTest+net.b{1})+net.b{2})
Other information :
My net.b[1] is: -1.16610230053776 1.16667147712026
My net.b[2] is: 51.3266249426358
And net.IW(1) is: 0.344272596370387 0.344111217766824
net.LW(2) is: 31.7635369693519 -31.8082184881063
When my inputTest is 3, the result of this command is 16, while it should be about 9. Have I made an error somewhere?
I found the Stack Overflow post Neural network in MATLAB that contains a problem like my problem, but there is a little difference, and the differences is in that problem the ranges of input and output are same, but in my problem is no. That solution says I need to scale out the results, but how can I scale out my result?
You are right about scaling. As was mentioned in the linked answer, the neural network by default scales the input and output to the range [-1,1]. This can be seen in the network processing functions configuration:
>> net = fitnet(2);
>> net.inputs{1}.processFcns
ans =
'removeconstantrows' 'mapminmax'
>> net.outputs{2}.processFcns
ans =
'removeconstantrows' 'mapminmax'
The second preprocessing function applied to both input/output is mapminmax with the following parameters:
>> net.inputs{1}.processParams{2}
ans =
ymin: -1
ymax: 1
>> net.outputs{2}.processParams{2}
ans =
ymin: -1
ymax: 1
to map both into the range [-1,1] (prior to training).
This means that the trained network expects input values in this range, and outputs values also in the same range. If you want to manually feed input to the network, and compute the output yourself, you have to scale the data at input, and reverse the mapping at the output.
One last thing to remember is that each time you train the ANN, you will get different weights. If you want reproducible results, you need to fix the state of the random number generator (initialize it with the same seed each time). Read the documentation on functions like rng and RandStream.
You also have to pay attention that if you are dividing the data into training/testing/validation sets, you must use the same split each time (probably also affected by the randomness aspect I mentioned).
Here is an example to illustrate the idea (adapted from another post of mine):
%%# data
x = linspace(-71,71,200); %# 1D input
y_model = x.^2; %# model
y = y_model + 10*randn(size(x)).*x; %# add some noise
%%# create ANN, train, simulate
net = fitnet(2); %# one hidden layer with 2 nodes
net.divideFcn = 'dividerand';
net.trainParam.epochs = 50;
net = train(net,x,y);
y_hat = net(x);
%%# plot
plot(x, y, 'b.'), hold on
plot(x, x.^2, 'Color','g', 'LineWidth',2)
plot(x, y_hat, 'Color','r', 'LineWidth',2)
legend({'data (noisy)','model (x^2)','fitted'})
hold off, grid on
%%# manually simulate network
%# map input to [-1,1] range
[~,inMap] = mapminmax(x, -1, 1);
in = mapminmax('apply', x, inMap);
%# propagate values to get output (scaled to [-1,1])
hid = tansig( bsxfun(#plus, net.IW{1}*in, net.b{1}) ); %# hidden layer
outLayerOut = purelin( net.LW{2}*hid + net.b{2} ); %# output layer
%# reverse mapping from [-1,1] to original data scale
[~,outMap] = mapminmax(y, -1, 1);
out = mapminmax('reverse', outLayerOut, outMap);
%# compare against MATLAB output
max( abs(out - y_hat) ) %# this should be zero (or in the order of `eps`)
I opted to use the mapminmax function, but you could have done that manually as well. The formula is a pretty simply linear mapping:
y = (ymax-ymin)*(x-xmin)/(xmax-xmin) + ymin;