Ver 0.38.0

- complex support (#35) - parallel support (#72) - More generic matrix - Update puruspe - Add some polynomials
Axect · Oct 31, 2024 · cf183cf · cf183cf
2 parents 8d7816b + 398eb2b
commit cf183cf
Show file tree

Hide file tree

Showing 88 changed files with 6,491 additions and 1,580 deletions.
diff --git a/Cargo.toml b/Cargo.toml
@@ -1,6 +1,6 @@
 [package]
 name = "peroxide"
-version = "0.37.9"
+version = "0.38.0"
 authors = ["axect <[email protected]>"]
 edition = "2018"
 description = "Rust comprehensive scientific computation library contains linear algebra, numerical analysis, statistics and machine learning tools with farmiliar syntax"
@@ -10,21 +10,29 @@ categories = ["science"]
 readme = "README.md"
 documentation = "https://axect.github.io/Peroxide_Doc"
 keywords = ["Numeric", "Science", "Dataframe", "Plot", "LinearAlgebra"]
-exclude = ["example_data/", "src/bin/", "benches/", "example/", "test_data/", "peroxide-ad2"]
+exclude = [
+    "example_data/",
+    "src/bin/",
+    "benches/",
+    "example/",
+    "test_data/",
+    "peroxide-ad2",
+]
 
 [badges]
 travis-ci = { repository = "axect/peroxide" }
 maintenance = { status = "actively-developed" }
 
 [dev-dependencies]
-float-cmp = "0.9"
+float-cmp = "0.10"
+criterion = { version = "0.5.1", features = ["html_reports"] }
 
 [dependencies]
 csv = { version = "1.3", optional = true, default-features = false }
 rand = { version = "0.8", features = ["small_rng"] }
 rand_distr = "0.4"
 order-stat = "0.1"
-puruspe = "0.2"
+puruspe = "0.3"
 matrixmultiply = { version = "0.3", features = ["threading"] }
 peroxide-ad = "0.3"
 peroxide-num = "0.1"
@@ -39,10 +47,10 @@ serde = { version = "1.0", features = ["derive"], optional = true }
 json = { version = "0.12", optional = true }
 arrow2 = { version = "0.18", features = ["io_parquet", "io_parquet_compression"], optional = true }
 num-complex = { version = "0.4", optional = true }
-lambert_w = { version = "0.3.0", default-features = false, features = ["24bits", "50bits"] }
+rayon = { version = "1.10", optional = true }
 
 [package.metadata.docs.rs]
-rustdoc-args = [ "--html-in-header", "katex-header.html", "--cfg", "docsrs"]
+rustdoc-args = ["--html-in-header", "katex-header.html", "--cfg", "docsrs"]
 
 [features]
 default = []
@@ -51,3 +59,4 @@ plot = ["pyo3"]
 nc = ["netcdf"]
 parquet = ["arrow2"]
 complex = ["num-complex", "matrixmultiply/cgemm"]
+parallel = ["rayon"]
diff --git a/README.md b/README.md
@@ -40,6 +40,8 @@ Peroxide provides various features.
 - `default` - Pure Rust (No dependencies of architecture - Perfect cross compilation)
 - `O3` - BLAS & LAPACK (Perfect performance but little bit hard to set-up - Strongly recommend to look [Peroxide with BLAS](https://github.com/Axect/Peroxide_BLAS))
 - `plot` - With matplotlib of python, we can draw any plots.
+- `complex` - With complex numbers (vector, matrix and integral)
+- `parallel` - With some parallel functions
 - `nc` - To handle netcdf file format with DataFrame
 - `csv` - To handle csv file format with Matrix or DataFrame
 - `parquet` - To handle parquet file format with DataFrame
@@ -311,7 +313,7 @@ How's that? Let me know if there's anything else you'd like me to improve!
 
 ## Latest README version
 
-Corresponding to `0.37.7`
+Corresponding to `0.38.0`
 
 ## Pre-requisite
 
@@ -321,55 +323,38 @@ Corresponding to `0.37.7`
 
 ## Install
 
-- Run below commands in your project directory
-
-1. Default
-
-    ```bash
-    cargo add peroxide
-    ```
-
-2. OpenBLAS
-
-    ```bash
-    cargo add peroxide --features O3
-    ```
-
-3. Plot
-
-    ```bash
-    cargo add peroxide --features plot
-    ```
-
-4. NetCDF dependency for DataFrame
-
-    ```bash
-    cargo add peroxide --features nc
-    ```
-
-5. CSV dependency for DataFrame
-
-    ```bash
-    cargo add peroxide --features csv
-    ```
+### Basic Installation
+```bash
+cargo add peroxide
+```
 
-6. Parquet dependency for DataFrame
+### Featured Installation
+```bash
+cargo add peroxide --features "<FEATURES>"
+```
 
-    ```bash
-    cargo add peroxide --features parquet
-    ```
+### Available Features
 
-7. Serialize or Deserialize with Matrix or polynomial
+* `O3`: Adds OpenBLAS support
+* `plot`: Enables plotting functionality
+* `complex`: Supports complex number operations
+* `parallel`: Enables parallel processing capabilities
+* `nc`: Adds NetCDF support for DataFrame
+* `csv`: Adds CSV support for DataFrame
+* `parquet`: Adds Parquet support for DataFrame
+* `serde`: Enables serialization/deserialization for Matrix and polynomial
 
-    ```bash
-    cargo add peroxide --features serde
-    ```
+### Install Examples
 
-8. All features
+Single feature installation:
+```bash
+cargo add peroxide --features "plot"
+```
 
-    ```bash
-    cargo add peroxide --features "O3 plot nc csv parquet serde"
-    ```
+Multiple features installation:
+```bash
+cargo add peroxide --features "O3 plot nc csv parquet serde"
+```
 
 ## Useful tips for features
 
@@ -388,6 +373,11 @@ Corresponding to `0.37.7`
 
 - __src__
   - [lib.rs](src/lib.rs) : `mod` and `re-export`
+  - __complex__: For complex vector, matrix & integrals.
+    - [mod.rs](src/complex/mod.rs)
+    - [integrate.rs](src/complex/integrate.rs) : Complex integral
+    - [matrix.rs](src/complex/matrix.rs) : Complex matrix
+    - [vector.rs](src/complex/vector.rs) : Complex vector
   - __fuga__ : Fuga for controlling numerical algorithms.
     - [mod.rs](src/fuga/mod.rs)
   - __macros__ : Macro files
@@ -434,6 +424,7 @@ Corresponding to `0.37.7`
     - [fp.rs](src/traits/fp.rs) : Functional programming toolbox
     - [general.rs](src/traits/general.rs) : General algorithms
     - [math.rs](src/traits/math.rs) : Mathematics
+    - [matrix.rs](src/traits/matrix.rs) : Matrix traits
     - [mutable.rs](src/traits/mutable.rs) : Mutable toolbox
     - [num.rs](src/traits/num.rs) : Number, Real and more operations
     - [pointer.rs](src/traits/pointer.rs) : Matrix pointer and Vector pointer for convenience

diff --git a/RELEASES.md b/RELEASES.md
@@ -1,3 +1,25 @@
+# Release 0.38.0
+
+## New features - Complex & Parallel
+
+- `complex` feature
+  - Implement complex vector, matrix and integral [#35](https://github.com/Axect/Peroxide/issues/35) (Thanks to [@GComitini](https://github.com/GComitini) and [@soumyasen1809](https://github.com/soumyasen1809))
+
+- `parallel` feature
+  - Implement some parallel functions [#72](https://github.com/Axect/Peroxide/issues/72) (Thanks to [@soumyasen1809](https://github.com/soumyasen1809))
+
+## Generic Matrix - MatrixTrait
+
+- Implement `MatrixTrait` for Matrix (Scalar = f64)
+- Implement `MatrixTrait` for ComplexMatrix (Scalar = C64)
+- `LinearAlgebra` and `solve` depend on `MatrixTrait`
+
+## Other changes
+
+- Update `puruspe` dependency to `0.3.0`, remove `lambert_w` dependency [#79](https://github.com/Axect/Peroxide/pull/79) (Thanks to [@JSorngard](https://github.com/JSorngard))
+
+- Add `hermite_polynomial` and `bessel_polynomial` [#80](https://github.com/Axect/Peroxide/pull/80) (Thanks to [@jgrage](https://github.com/jgrage))
+
 # Release 0.37.9 (2024-07-31)
 
 - Fix inconsistent lambert w function name [#65](https://github.com/Axect/Peroxide/issues/65) (Thanks to [@JSorngard](https://github.com/JSorngard))

diff --git a/benches/data/rayon_matrix_benchmark_results.md b/benches/data/rayon_matrix_benchmark_results.md
@@ -0,0 +1,194 @@
+Lib used for benchmarking: Criterion
+Matrix size: 1000x1000
+
+Running benches/parallel_rayon/matrix_benchmark.rs
+
+ser_matrix_bench        time:   [535.12 µs 544.51 µs 556.68 µs]
+Found 11 outliers among 100 measurements (11.00%)
+  4 (4.00%) high mild
+  7 (7.00%) high severe
+
+par_matrix_bench        time:   [5.0912 ms 5.1431 ms 5.1995 ms]
+Found 7 outliers among 100 measurements (7.00%)
+  1 (1.00%) low mild
+  5 (5.00%) high mild
+  1 (1.00%) high severe
+
+ser_py_matrix_bench     time:   [4.3100 ms 4.3309 ms 4.3544 ms]
+Found 7 outliers among 100 measurements (7.00%)
+  2 (2.00%) high mild
+  5 (5.00%) high severe
+
+par_py_matrix_bench     time:   [11.667 ms 11.789 ms 11.920 ms]
+Found 10 outliers among 100 measurements (10.00%)
+  6 (6.00%) high mild
+  4 (4.00%) high severe
+
+ser_matrix_change_shape_bench
+                        time:   [7.3630 ms 7.4075 ms 7.4608 ms]
+Found 5 outliers among 100 measurements (5.00%)
+  1 (1.00%) high mild
+  4 (4.00%) high severe
+
+par_matrix_change_shape_bench
+                        time:   [10.276 ms 10.385 ms 10.499 ms]
+Found 3 outliers among 100 measurements (3.00%)
+  2 (2.00%) high mild
+  1 (1.00%) high severe
+
+ser_matrix_extract_row_bench
+                        time:   [613.39 µs 622.44 µs 633.72 µs]
+Found 7 outliers among 100 measurements (7.00%)
+  7 (7.00%) high severe
+
+par_matrix_extract_row_bench
+                        time:   [5.4321 ms 5.4851 ms 5.5399 ms]
+Found 4 outliers among 100 measurements (4.00%)
+  4 (4.00%) high mild
+
+ser_matrix_from_index_bench
+                        time:   [2.4174 ms 2.4490 ms 2.4851 ms]
+Found 14 outliers among 100 measurements (14.00%)
+  1 (1.00%) high mild
+  13 (13.00%) high severe
+
+par_matrix_from_index_bench
+                        time:   [2.3912 ms 2.4090 ms 2.4304 ms]
+Found 9 outliers among 100 measurements (9.00%)
+  2 (2.00%) high mild
+  7 (7.00%) high severe
+
+ser_matrix_to_vec_bench time:   [2.4800 ms 2.5082 ms 2.5423 ms]
+Found 10 outliers among 100 measurements (10.00%)
+  4 (4.00%) high mild
+  6 (6.00%) high severe
+
+par_matrix_to_vec_bench time:   [6.4041 ms 6.4618 ms 6.5250 ms]
+Found 6 outliers among 100 measurements (6.00%)
+  5 (5.00%) high mild
+  1 (1.00%) high severe
+
+ser_matrix_to_diag_bench
+                        time:   [2.4335 ms 2.4526 ms 2.4750 ms]
+Found 14 outliers among 100 measurements (14.00%)
+  6 (6.00%) high mild
+  8 (8.00%) high severe
+
+par_matrix_to_diag_bench
+                        time:   [13.514 ms 13.684 ms 13.868 ms]
+Found 10 outliers among 100 measurements (10.00%)
+  7 (7.00%) high mild
+  3 (3.00%) high severe
+
+Benchmarking ser_matrix_submat_bench: Warming up for 3.0000 s
+Warning: Unable to complete 100 samples in 5.0s. You may wish to increase target time to 8.3s, enable flat sampling, or reduce sample count to 50.
+ser_matrix_submat_bench time:   [1.6077 ms 1.6243 ms 1.6451 ms]
+Found 16 outliers among 100 measurements (16.00%)
+  3 (3.00%) high mild
+  13 (13.00%) high severe
+
+par_matrix_submat_bench time:   [10.611 ms 10.761 ms 10.942 ms]
+Found 5 outliers among 100 measurements (5.00%)
+  3 (3.00%) high mild
+  2 (2.00%) high severe
+
+ser_matrix_add_vec_bench
+                        time:   [7.3077 ms 7.3485 ms 7.3946 ms]
+Found 12 outliers among 100 measurements (12.00%)
+  2 (2.00%) high mild
+  10 (10.00%) high severe
+
+par_matrix_add_vec_bench
+                        time:   [11.331 ms 11.480 ms 11.636 ms]
+Found 2 outliers among 100 measurements (2.00%)
+  2 (2.00%) high mild
+
+ser_matrix_norm_bench   time:   [5.1600 ms 5.1864 ms 5.2165 ms]
+Found 7 outliers among 100 measurements (7.00%)
+  1 (1.00%) high mild
+  6 (6.00%) high severe
+
+par_matrix_norm_bench   time:   [2.6565 ms 2.6810 ms 2.7091 ms]
+Found 5 outliers among 100 measurements (5.00%)
+  2 (2.00%) high mild
+  3 (3.00%) high severe
+
+Benchmarking ser_matrix_norm_bench #2: Warming up for 3.0000 s
+Warning: Unable to complete 100 samples in 5.0s. You may wish to increase target time to 8.9s, enable flat sampling, or reduce sample count to 50.
+ser_matrix_norm_bench #2
+                        time:   [1.7262 ms 1.7391 ms 1.7541 ms]
+Found 15 outliers among 100 measurements (15.00%)
+  10 (10.00%) high mild
+  5 (5.00%) high severe
+
+par_matrix_norm_bench #2
+                        time:   [6.7071 ms 6.7883 ms 6.8703 ms]
+Found 1 outliers among 100 measurements (1.00%)
+  1 (1.00%) high mild
+
+ser_matrix_norm_bench #3
+                        time:   [9.7582 ms 9.9006 ms 10.057 ms]
+Found 12 outliers among 100 measurements (12.00%)
+  5 (5.00%) high mild
+  7 (7.00%) high severe
+
+par_matrix_norm_bench #3
+                        time:   [9.3004 ms 9.4088 ms 9.5239 ms]
+Found 1 outliers among 100 measurements (1.00%)
+  1 (1.00%) high mild
+
+ser_matrix_inner_prod_bench
+                        time:   [5.2730 ms 5.3590 ms 5.4583 ms]
+Found 14 outliers among 100 measurements (14.00%)
+  3 (3.00%) high mild
+  11 (11.00%) high severe
+
+par_matrix_inner_prod_bench
+                        time:   [5.0987 ms 5.1644 ms 5.2402 ms]
+Found 7 outliers among 100 measurements (7.00%)
+  3 (3.00%) high mild
+  4 (4.00%) high severe
+
+ser_matrix_hadamard_bench
+                        time:   [5.6521 ms 5.6870 ms 5.7262 ms]
+Found 12 outliers among 100 measurements (12.00%)
+  3 (3.00%) high mild
+  9 (9.00%) high severe
+
+par_matrix_hadamard_bench
+                        time:   [14.155 ms 14.335 ms 14.527 ms]
+Found 4 outliers among 100 measurements (4.00%)
+  3 (3.00%) high mild
+  1 (1.00%) high severe
+
+ser_matrix_take_row_bench
+                        time:   [3.7894 ms 3.8234 ms 3.8613 ms]
+Found 15 outliers among 100 measurements (15.00%)
+  7 (7.00%) high mild
+  8 (8.00%) high severe
+
+par_matrix_take_row_bench
+                        time:   [8.4008 ms 8.5171 ms 8.6523 ms]
+Found 9 outliers among 100 measurements (9.00%)
+  6 (6.00%) high mild
+  3 (3.00%) high severe
+
+ser_matrix_fpmap_bench  time:   [3.2526 ms 3.2739 ms 3.2977 ms]
+Found 12 outliers among 100 measurements (12.00%)
+  2 (2.00%) high mild
+  10 (10.00%) high severe
+
+par_matrix_fpmap_bench  time:   [10.604 ms 10.765 ms 10.937 ms]
+Found 11 outliers among 100 measurements (11.00%)
+  8 (8.00%) high mild
+  3 (3.00%) high severe
+
+ser_matrix_reduce_bench time:   [2.6748 ms 2.6964 ms 2.7201 ms]
+Found 9 outliers among 100 measurements (9.00%)
+  6 (6.00%) high mild
+  3 (3.00%) high severe
+
+par_matrix_reduce_bench time:   [6.2453 ms 6.3198 ms 6.4034 ms]
+Found 6 outliers among 100 measurements (6.00%)
+  4 (4.00%) high mild
+  2 (2.00%) high severe