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[WIP] Bs feature branch #557

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[WIP] Bs feature branch #557

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4f50a53
Linalg solver
bshmu Jan 29, 2024
6bee419
linalg solver
bshmu Jan 29, 2024
47abc02
mean function
bshmu Feb 11, 2024
202dcfb
updates
bshmu Feb 19, 2024
2c6beda
Updates 2/20
bshmu Feb 20, 2024
c14359b
Updates 2/21
bshmu Feb 21, 2024
b3d0d1e
Updates 2/22
Feb 22, 2024
67787e3
2/24
bshmu Feb 25, 2024
107c281
Updates
Feb 25, 2024
d7b7cc0
Updates 2/25
bshmu Feb 25, 2024
49e7ba8
Updates
bshmu Feb 25, 2024
5ec5b10
Updates
bshmu Feb 25, 2024
cea0331
Updates
bshmu Feb 25, 2024
8d404b1
Updates
bshmu Feb 25, 2024
e3b8380
Updates
Feb 26, 2024
35d51a5
Final updates
Feb 26, 2024
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2 changes: 1 addition & 1 deletion .tool-versions
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Original file line number Diff line number Diff line change
@@ -1 +1 @@
scarb 2.4.0
scarb 2.4.0
2 changes: 1 addition & 1 deletion src/lib.cairo
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@@ -1,4 +1,4 @@
mod operators;
mod numbers;
mod utils;
mod test_helper;
mod test_helper;
346 changes: 3 additions & 343 deletions src/operators/matrix.cairo
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@@ -1,343 +1,3 @@
use core::array::ArrayTrait;
use core::option::OptionTrait;

use orion::numbers::NumberTrait;
use orion::operators::vec::{VecTrait, NullableVec, NullableVecImpl};

struct MutMatrix<T> {
data: NullableVec<T>,
rows: usize,
cols: usize,
}

impl MutMatrixDestruct<T, +Drop<T>> of Destruct<MutMatrix<T>> {
fn destruct(self: MutMatrix<T>) nopanic {
self.data.destruct()
}
}

#[generate_trait]
impl MutMatrixImpl<
T, MAG, +Drop<T>, +Copy<T>, +NumberTrait<T, MAG>, +PartialOrd<T>
> of MutMatrixTrait<T> {
/// Constructor for the Matrix
fn new(rows: usize, cols: usize) -> MutMatrix<T> {
MutMatrix { data: NullableVecImpl::new(), rows: rows, cols: cols }
}

/// Get the value at (row, col)
fn get(ref self: MutMatrix<T>, row: usize, col: usize) -> Option<T> {
if row >= self.rows || col >= self.cols {
Option::None
} else {
self.data.get(row * self.cols + col)
}
}

/// Set the value at (row, col)
fn set(ref self: MutMatrix<T>, row: usize, col: usize, value: T) {
if row < self.rows && col < self.cols {
let index = row * self.cols + col;
self.data.set(index, value)
}
}

/// Returns the shape of the matrix as (rows, cols)
fn shape(self: MutMatrix<T>) -> (usize, usize) {
(self.rows, self.cols)
}

/// Returns the index of the maximum value along the specified axis
fn argmax(ref self: MutMatrix<T>, axis: usize) -> Span<usize> {
assert(axis < 2, 'Invalid axis');

let mut result: Array<usize> = ArrayTrait::new();
if axis == 0 {
let mut col: usize = 0;
loop {
if col == self.cols {
break;
}

let mut max_value = self.get(0, col);
let mut max_value = match max_value {
Option::Some => { max_value.unwrap() },
Option::None => { NumberTrait::min_value() }
};
let mut max_index = 0;

let mut row: usize = 1;
loop {
if row == self.rows {
break;
}

let mut value = self.get(row, col);
let mut value = match value {
Option::Some => { value.unwrap() },
Option::None => { NumberTrait::min_value() }
};
if value > max_value {
max_value = value;
max_index = row;
}

row += 1;
};

result.append(max_index);

col += 1;
};

return result.span();
}

let mut row: usize = 0;
loop {
if row == self.rows {
break;
}

let mut max_value = self.get(row, 0);
let mut max_value = match max_value {
Option::Some => { max_value.unwrap() },
Option::None => { NumberTrait::min_value() }
};
let mut max_index = 0;

let mut col: usize = 1;
loop {
if col == self.cols {
break;
}

let mut value = self.get(row, col);
let mut value = match value {
Option::Some => { value.unwrap() },
Option::None => { NumberTrait::min_value() }
};
if value > max_value {
max_value = value;
max_index = col;
}

col += 1;
};

result.append(max_index);

row += 1;
};

return result.span();
}

/// Apply softmax to the matrix along the specified axis
fn softmax<+AddEq<T>, +Div<T>>(ref self: MutMatrix<T>, axis: usize) -> MutMatrix<T> {
assert(axis < 2, 'Invalid axis');

let mut result = MutMatrixImpl::new(self.rows, self.cols);

if axis == 0 {
let mut col: usize = 0;
loop {
if col == self.cols {
break;
}

let mut sum_exp = NumberTrait::zero();
let mut row: usize = 0;
loop {
if row == self.rows {
break;
}

let value = self.get(row, col).unwrap().into();
sum_exp += value.exp();

row += 1;
};

row = 0;
loop {
if row == self.rows {
break;
}

let value = self.get(row, col).unwrap().into();
let softmax_value = (value.exp() / sum_exp).into();
result.set(row, col, softmax_value);

row += 1;
};

col += 1;
};
} else {
let mut row: usize = 0;
loop {
if row == self.rows {
break;
}

let mut sum_exp = NumberTrait::zero();
let mut col: usize = 0;
loop {
if col == self.cols {
break;
}

let value = self.get(row, col).unwrap().into();
sum_exp += value.exp();

col += 1;
};

col = 0;
loop {
if col == self.cols {
break;
}

let value = self.get(row, col).unwrap().into();
let softmax_value = (value.exp() / sum_exp).into();
result.set(row, col, softmax_value);

col += 1;
};

row += 1;
};
}

result
}

/// Apply softmax to the matrix along the specified axis, treating zeros as neutral
fn softmax_zero<+AddEq<T>, +Div<T>, +PartialEq<T>>(
ref self: MutMatrix<T>, axis: usize
) -> MutMatrix<T> {
assert(axis < 2, 'Invalid axis');

let mut result = MutMatrixImpl::new(self.rows, self.cols);

if axis == 0 {
let mut col: usize = 0;
loop {
if col == self.cols {
break;
}

let mut sum_exp = NumberTrait::zero();
let mut row: usize = 0;
loop {
if row == self.rows {
break;
}

let value = self.get(row, col).unwrap().into();
if value != NumberTrait::zero() {
sum_exp += value.exp();
}
row += 1;
};

row = 0;
loop {
if row == self.rows {
break;
}

let value = self.get(row, col).unwrap().into();
if value != NumberTrait::zero() {
let softmax_value = (value.exp() / sum_exp).into();
result.set(row, col, softmax_value);
} else {
result.set(row, col, NumberTrait::zero());
}

row += 1;
};

col += 1;
};
} else {
let mut row: usize = 0;
loop {
if row == self.rows {
break;
}

let mut sum_exp = NumberTrait::zero();
let mut col: usize = 0;
loop {
if col == self.cols {
break;
}

let value = self.get(row, col).unwrap().into();
if value != NumberTrait::zero() {
sum_exp += value.exp();
}
col += 1;
};

col = 0;
loop {
if col == self.cols {
break;
}

let value = self.get(row, col).unwrap().into();

if value != NumberTrait::zero() {
let softmax_value = (value.exp() / sum_exp).into();
result.set(row, col, softmax_value);
} else {
result.set(row, col, NumberTrait::zero());
}

col += 1;
};

row += 1;
};
}

result
}

/// Apply the sigmoid function to each element of the matrix
fn sigmoid<+Mul<T>, +Add<T>, +Div<T>>(ref self: MutMatrix<T>) -> MutMatrix<T> {
let mut result = MutMatrixImpl::new(self.rows, self.cols);

let mut row: usize = 0;
loop {
if row == self.rows {
break;
}

let mut col: usize = 0;
loop {
if col == self.cols {
break;
}

let value = self.get(row, col);
if value.is_some() {
let value = NumberTrait::one()
/ (NumberTrait::one() + (value.unwrap() * NumberTrait::neg_one()).exp());

result.set(row, col, value);
}

col += 1;
};

row += 1;
};

result
}
}
mod matrix;
mod matrix_linalg;
mod matrix_statistics;
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