| #include <stdio.h> | |
| #include <math.h> | |
| const int buffSize = 4; | |
| const float false_rate = 0.01; | |
| float train_cell1[buffSize] = {-0.1980671, 0.9246765, 0.3704300, 1.1070784}; | |
| float train_cell2[buffSize] = {-0.02937893, -0.90626937, -0.85622522, 0.49817418}; | |
| float test_cell[buffSize] = {1.87743553, 0.02268982, -1.41455143, 0.54926504}; |
| // | |
| // Fourth order bandpass IIR filter coefficients (100 Hz - 500 Hz, sampling rate 22050 Hz) | |
| // | |
| // a[0]*y[n] + a[1]*y[n-1] + … + a[n]*y[0] = b[0]*x[n] + b[1]*x[m-1] + … + b[m]*x[0] | |
| // | |
| #include <stdio.h> | |
| const double a[5] = {1, -3.91745514489661, 5.75755576410991, -3.76266550602334, 0.922565876650813}; | |
| const double b[5] = {0.000780326282260527, 0, -0.00156065256452105, 0, 0.000780326282260527}; |
Generate the list yourself:
$ cd /Applications/Xcode.app/Contents/Developer/Platforms/iPhoneOS.platform/Developer/SDKs/iPhoneOS*.sdk/System/Library/Frameworks/UIKit.framework/Headers
$ grep -H UI_APPEARANCE_SELECTOR ./* | sed 's/ __OSX_AVAILABLE_STARTING(__MAC_NA,__IPHONE_5_0) UI_APPEARANCE_SELECTOR;//'
| gar.fun<-function(out.var,mod.in,bar.plot=T,x.names=NULL,...){ | |
| require(devtools) | |
| source_gist('5086859') | |
| best.wts<-plot.nnet(mod.in,wts.only=T) | |
| out.ind<-which(out.var==colnames(eval(mod.in$call$y))) | |
| #get input-hidden weights and hidden-output weights, remove bias | |
| inp.hid<-data.frame( |
| plot.nnet<-function(mod.in,nid=T,all.out=T,all.in=T,wts.only=F,rel.rsc=5,circle.cex=5,node.labs=T, | |
| line.stag=NULL,cex.val=1,alpha.val=1,circle.col='lightgrey',pos.col='black',neg.col='grey',...){ | |
| require(scales) | |
| #gets weights for neural network, output is list | |
| #if rescaled argument is true, weights are returned but rescaled based on abs value | |
| nnet.vals<-function(mod.in,nid,rel.rsc){ | |
| library(scales) |