ArrayFire Ruby Bindings
(Part V : Statistics and Random Engine routines)
This post explains about the Statistics and Random class of arrayfire gem.
ArrayFire::Statistics class consists of methods that can be used to operate on Af_Array for statistical analysis. ArrayFire::Random class contains of methods to generate Random numbers and array of Random numbers
on the GPU.
Statistics Class
The Statistics class contains of singleton methods like mean, var, median, stddev, etc.
Let us take a look at the implementation.
void Init_arrayfire() {
ArrayFire = rb_define_module("ArrayFire");
Statistics = rb_define_class_under(ArrayFire, "Statistics", rb_cObject);
rb_define_singleton_method(Statistics, "mean", (METHOD)arf_mean, 2);
rb_define_singleton_method(Statistics, "mean_weighted", (METHOD)arf_mean_weighted, 3);
rb_define_singleton_method(Statistics, "var", (METHOD)arf_var, 3);
rb_define_singleton_method(Statistics, "var_weighted", (METHOD)arf_var_weighted, 3);
rb_define_singleton_method(Statistics, "stdev", (METHOD)arf_stdev, 2);
rb_define_singleton_method(Statistics, "cov", (METHOD)arf_cov, 3);
rb_define_singleton_method(Statistics, "median", (METHOD)arf_median, 2);
rb_define_singleton_method(Statistics, "mean_all", (METHOD)arf_mean_all, 1);
rb_define_singleton_method(Statistics, "mean_all_weighted", (METHOD)arf_mean_all_weighted, 2);
rb_define_singleton_method(Statistics, "var_all", (METHOD)arf_var_all, 2);
rb_define_singleton_method(Statistics, "var_all_weighted", (METHOD)arf_var_all_weighted, 2);
rb_define_singleton_method(Statistics, "stdev_all", (METHOD)arf_stdev_all, 1);
rb_define_singleton_method(Statistics, "median_all", (METHOD)arf_median_all, 1);
rb_define_singleton_method(Statistics, "corrcoef", (METHOD)arf_corrcoef, 2);
}
ArrayFire provides four different methods to calculate the means namely,
mean, mean_weighted, mean_all, mean_all_weighted. The implementation
is given below.
mean and mean_weighted calculate the mean and weighted mean along the dimnension
specified. However, mean_all and mean_weighted_all calculate the mean and weighted
mean over all the elements in an array.
static VALUE arf_mean(VALUE self, VALUE array_val, VALUE dim_val){
afstruct* input;
afstruct* output = ALLOC(afstruct);
Data_Get_Struct(array_val, afstruct, input);
af_mean(&output->carray, input->carray, NUM2UINT(dim_val));
af_print_array(output->carray);
return Data_Wrap_Struct(Af_Array, NULL, arf_free, output);
}
static VALUE arf_mean_weighted(VALUE self, VALUE array_val, VALUE weighted_array_val, VALUE dim_val){
afstruct* input;
afstruct* weighted_array;
afstruct* output = ALLOC(afstruct);
Data_Get_Struct(array_val, afstruct, input);
Data_Get_Struct(weighted_array_val, afstruct, weighted_array);
af_mean_weighted(&output->carray, input->carray, weighted_array->carray, NUM2UINT(dim_val));
af_print_array(output->carray);
return Data_Wrap_Struct(Af_Array, NULL, arf_free, output);
}
static VALUE arf_mean_all(VALUE self, VALUE array_val){
afstruct* input;
double real_part, imag_part;
Data_Get_Struct(array_val, afstruct, input);
af_mean_all(&real_part, &imag_part, input->carray);
return DBL2NUM(real_part);
}
static VALUE arf_mean_all_weighted(VALUE self, VALUE array_val, VALUE weighted_array_val){
afstruct* input;
afstruct* weighted_array;
double real_part, imag_part;
Data_Get_Struct(array_val, afstruct, input);
Data_Get_Struct(weighted_array_val, afstruct, weighted_array);
af_mean_all_weighted(&real_part, &imag_part, input->carray, weighted_array->carray);
return DBL2NUM(real_part);
}
I check the bindings using pry.
$ rake pry
pry -r './lib/arrayfire.rb'
[1] pry(main)> arr = ArrayFire::Random.randu(2, [4, 4])=> #<ArrayFire::Af_Array:0x0000000308fb08>
[2] pry(main)> ArrayFire::Util.print_array(arr)No Name Array
[4 4 1 1]
0.3990 0.7353 0.9455 0.7089
0.6720 0.5160 0.1587 0.9434
0.5339 0.3932 0.8831 0.1227
0.1386 0.2706 0.0621 0.9107
=> true
[3] pry(main)> weighted_array = ArrayFire::Af_Array.new 2, [4,4], [1, 2, 2, 0, -2, 2 , 1, 3, 1, 4, 3 , 1, 0, -3, 2, 9]
No Name Array
[4 4 1 1]
1.0000 -2.0000 1.0000 0.0000
2.0000 2.0000 4.0000 -3.0000
2.0000 1.0000 3.0000 2.0000
0.0000 3.0000 1.0000 9.0000
=> #<ArrayFire::Af_Array:0x0000000316c008>
[4] pry(main)> mean = ArrayFire::Statistics.mean(arr, 1)
No Name Array
[4 1 1 1]
0.6972
0.5725
0.4832
0.3455
=> #<ArrayFire::Af_Array:0x00000003212138>
[5] pry(main)> mean_weighted = ArrayFire::Statistics.mean_weighted(arr, weighted_array, 2)
No Name Array
[4 4 1 1]
0.3990 0.7353 0.9455 nan
0.6720 0.5160 0.1587 0.9434
0.5339 0.3932 0.8831 0.1227
nan 0.2706 0.0621 0.9107
=> #<ArrayFire::Af_Array:0x000000031d2588>
[6] pry(main)> mean_all = ArrayFire::Statistics.mean_all(arr)=> 0.5246008634567261
[7] pry(main)> mean_all_weighted = ArrayFire::Statistics.mean_all_weighted(arr, weighted_array)
=> 0.5184718370437622
Random Class
ArrayFire supports three types of random engines: :AF_RANDOM_ENGINE_PHILOX_4X32_10,
:AF_RANDOM_ENGINE_THREEFRY_2X32_16 and :AF_RANDOM_ENGINE_MERSENNE_GP11213 which can be
passed as a type.
The default randon engine is :AF_RANDOM_ENGINE_PHILOX_4X32_10. Random class helps in creating
an engine by specifying the type and seed. A programmer can set the seed on the fly and also create
arrays with randomly generated values.
void Init_arrayfire() {
ArrayFire = rb_define_module("ArrayFire");
Random = rb_define_class_under(ArrayFire, "Random", rb_cObject);
rb_define_alloc_func(Random, arf_engine_alloc);
rb_define_singleton_method(Random, "create_random_engine", (METHOD)arf_create_random_engine, 2);
rb_define_singleton_method(Random, "retain_random_engine", (METHOD)arf_retain_random_engine, 1);
rb_define_singleton_method(Random, "random_engine_set_type", (METHOD)arf_random_engine_set_type, 0);
rb_define_singleton_method(Random, "random_engine_get_type", (METHOD)arf_random_engine_get_type, 0);
rb_define_singleton_method(Random, "random_uniform", (METHOD)arf_random_uniform, 3);
rb_define_singleton_method(Random, "random_normal", (METHOD)arf_random_normal, 3);
rb_define_singleton_method(Random, "random_engine_set_seed", (METHOD)arf_random_engine_set_seed, 2);
rb_define_singleton_method(Random, "get_default_random_engine", (METHOD)arf_get_default_random_engine, 0);
rb_define_singleton_method(Random, "set_default_random_engine_type", (METHOD)arf_set_default_random_engine_type, 0);
rb_define_singleton_method(Random, "random_engine_get_seed", (METHOD)arf_random_engine_get_seed, 0);
rb_define_singleton_method(Random, "release_random_engine", (METHOD)arf_release_random_engine, 0);
rb_define_singleton_method(Random, "randu", (METHOD)arf_randu, 2);
rb_define_singleton_method(Random, "randn", (METHOD)arf_randn, 2);
rb_define_singleton_method(Random, "set_seed", (METHOD)arf_set_seed, 1);
rb_define_singleton_method(Random, "get_seed", (METHOD)arf_get_seed, 0);
}
A Random enigne must be created so I write the ruby bindings to alloc memory
and dealloc memory to a af_random_engine using arf_engine_alloc and arf_engine_free
respectively. The rest is similar to how I created bindings in previous blog posts.
typedef struct RANDOM_ENGINE_STRUCT
{
af_random_engine cengine;
}afrandomenginestruct;
static VALUE arf_engine_alloc(VALUE klass)
{
/* allocate */
afrandomenginestruct* afrandomengine = ALLOC(afrandomenginestruct);
/* wrap */
return Data_Wrap_Struct(klass, NULL, arf_engine_free, afrandomengine);
}
static void arf_engine_free(afrandomenginestruct* afrandomengine)
{
free(afrandomengine);
}
static VALUE arf_create_random_engine(VALUE self, VALUE type_val, VALUE seed_val){
afrandomenginestruct* output = ALLOC(afrandomenginestruct);
af_random_engine_type rtype = arf_randome_engine_type_from_rbsymbol(type_val);
af_create_random_engine(&output->cengine, AF_RANDOM_ENGINE_DEFAULT, NUM2ULL(seed_val) ) ;
return Data_Wrap_Struct(Random, NULL, arf_engine_free, output);
}
static VALUE arf_randu(VALUE self, VALUE ndims_val, VALUE dim_val){
afstruct* out_array = ALLOC(afstruct);
dim_t ndims = (dim_t)FIX2LONG(ndims_val);
dim_t* dimensions = (dim_t*)malloc(ndims * sizeof(dim_t));
dim_t count = 1;
for (dim_t index = 0; index < ndims; index++) {
dimensions[index] = (dim_t)FIX2LONG(RARRAY_AREF(dim_val, index));
count *= dimensions[index];
}
af_randu(&out_array->carray, ndims, dimensions,f64);
return Data_Wrap_Struct(Af_Array, NULL, arf_free, out_array);
}
Now, we have the bindings ready, we can check it using pry.
$ rake pry
pry -r './lib/arrayfire.rb'
[1] pry(main)> engine = ArrayFire::Random.create_random_engine(:AF_RANDOM_ENGINE_PHILOX_4X32_10, 100)
=> #<ArrayFire::Random:0x00000002a41350>
[2] pry(main)> ArrayFire::Random.random_engine_get_seed(engine)
=> 100
[3] pry(main)> ArrayFire::Random.random_engine_set_seed(engine, 123)
=> #<ArrayFire::Random:0x00000002cfbd98>
[4] pry(main)> ArrayFire::Random.random_engine_get_seed(engine)
=> 123
[5] pry(main)> ArrayFire::Random.random_engine_get_type engine
=> "AF_RANDOM_ENGINE_PHILOX_4X32_10"
[6] pry(main)> arr = ArrayFire::Random.randu(2, [4, 4])
=> #<ArrayFire::Af_Array:0x00000002b185f8>
[7] pry(main)> ArrayFire::Util.print_array(arr)
No Name Array
[4 4 1 1]
0.3990 0.7353 0.9455 0.7089
0.6720 0.5160 0.1587 0.9434
0.5339 0.3932 0.8831 0.1227
0.1386 0.2706 0.0621 0.9107
=> true
[8] pry(main)> arr2 = ArrayFire::Random.randn(2, [4, 4])
=> #<ArrayFire::Af_Array:0x000000029fd4c0>
[9] pry(main)> ArrayFire::Util.print_array(arr2)
No Name Array
[4 4 1 1]
0.2985 -0.8873 -1.0309 0.5312
-2.7126 -0.3550 -1.4627 -1.7783
0.4584 1.9841 0.0075 -0.5459
1.5579 -0.9308 -1.0512 0.4640
=> true
we have the Random support ready for ArrayFire-rb
Hence, Ruby bindings for Statistics and Random methods have been successfully implemented.
Conclusion
We can now use ArrayFire-rb for statistical analysis on data. Since, the calculations would be
on GPU, we can feed it large amount of data from real world.
Random engine can help in creating large arrays with randomly generated values in seconds.
In the next blog, I will explain about using the Device and Util class to manage GPU devices
and device memory and other utilities like saving an Af_Array to file.
