trigonometric.md
In cardinalR: Collection of Data Structures

gen_crescent() works

Code
  gen_crescent(n = 500, p = 4)
Message
  v Wavy shaped noise dimensions generation completed successfully!!!
  v Data generation completed successfully!!!
Output
  # A tibble: 500 x 4
        x1    x2    x3    x4
     <dbl> <dbl> <dbl> <dbl>
   1 0.866 0.5   0.340  2.43
   2 0.860 0.510 0.971  2.00
   3 0.854 0.520 0.503  1.45
   4 0.848 0.530 0.632  2.23
   5 0.842 0.539 0.453  1.55
   6 0.836 0.549 0.109  1.91
   7 0.829 0.559 0.717  2.13
   8 0.823 0.568 0.565  2.44
   9 0.816 0.578 1.34   2.08
  10 0.810 0.587 1.27   2.27
  # i 490 more rows

gen_curvycylinder() works

Code
  gen_curvycylinder(n = 500, p = 4, h = 10)
Message
  v Data generation completed successfully!!!
Output
  # A tibble: 500 x 4
         x1     x2    x3     x4
      <dbl>  <dbl> <dbl>  <dbl>
   1 -0.634  0.773  4.02 -0.770
   2  0.610 -0.793  7.42  0.908
   3 -0.773  0.635  1.72  0.988
   4 -0.976 -0.218  6.00 -0.280
   5  0.602  0.799  8.07  0.977
   6  0.234  0.972  3.52 -0.373
   7 -0.592  0.806  6.84  0.528
   8 -0.319 -0.948  2.35  0.709
   9  0.983 -0.183  3.72 -0.548
  10  0.527 -0.850  9.23  0.196
  # i 490 more rows

gen_sphericalspiral() works

Code
  gen_sphericalspiral(n = 500, p = 4, spins = 1)
Message
  v Data generation completed successfully!!!
Output
  # A tibble: 500 x 4
          x1        x2    x3      x4
       <dbl>     <dbl> <dbl>   <dbl>
   1 0       0         1.41  0      
   2 0.00630 0.0000793 1.07  0.00200
   3 0.0126  0.000317  0.760 0.00401
   4 0.0189  0.000713  0.856 0.00601
   5 0.0251  0.00127   0.598 0.00802
   6 0.0314  0.00198   1.31  0.0100 
   7 0.0377  0.00285   1.40  0.0120 
   8 0.0439  0.00388   0.965 0.0140 
   9 0.0501  0.00506   1.15  0.0160 
  10 0.0563  0.00640   1.06  0.0180 
  # i 490 more rows

gen_helicalspiral() works

Code
  gen_helicalspiral(n = 500, p = 4)
Message
  v Data generation completed successfully!!!
Output
  # A tibble: 500 x 4
        x1      x2      x3       x4
     <dbl>   <dbl>   <dbl>    <dbl>
   1 1     0        0.406  0       
   2 1.000 0.00787  0.0700 0.000787
   3 1.000 0.0157  -0.239  0.00157 
   4 1.000 0.0236  -0.142  0.00236 
   5 1.000 0.0315  -0.400  0.00315 
   6 0.999 0.0393   0.310  0.00393 
   7 0.999 0.0472   0.403  0.00472 
   8 0.998 0.0551  -0.0317 0.00551 
   9 0.998 0.0629   0.150  0.00629 
  10 0.997 0.0708   0.0625 0.00708 
  # i 490 more rows

gen_conicspiral() works

Code
  gen_conicspiral(n = 500, p = 4, spins = 1)
Message
  v Data generation completed successfully!!!
Output
  # A tibble: 500 x 4
         x1       x2      x3     x4
      <dbl>    <dbl>   <dbl>  <dbl>
   1 0      0         0.534  0.181 
   2 0.0126 0.000159  0.303  0.420 
   3 0.0252 0.000634  0.0903 0.0219
   4 0.0377 0.00143   0.162  0.323 
   5 0.0503 0.00254  -0.0152 0.470 
   6 0.0628 0.00396   0.486  0.155 
   7 0.0753 0.00570   0.554  0.390 
   8 0.0878 0.00776   0.254  0.0802
   9 0.100  0.0101    0.385  0.181 
  10 0.113  0.0128    0.327  0.571 
  # i 490 more rows

gen_nonlinear() works

Code
  gen_nonlinear(n = 500, p = 4, hc = 1, non_fac = 0.5)
Message
  v Data generation completed successfully!!!
Output
  # A tibble: 500 x 4
        x1    x2    x3     x4
     <dbl> <dbl> <dbl>  <dbl>
   1 1.82   1.03 0.381  0.915
   2 1.18   1.31 0.620 -0.923
   3 0.595  1.96 0.221 -0.368
   4 0.778  1.64 0.520 -0.685
   5 0.287  3.63 0.665  0.678
   6 1.64   1.11 0.347  0.442
   7 1.81   1.04 0.579  0.859
   8 0.985  1.43 0.265 -1.06 
   9 1.33   1.24 0.361 -0.564
  10 1.16   1.32 0.746 -0.824
  # i 490 more rows