From c1efc3e2df6e2aaf4a1672590a404143203885a6 Mon Sep 17 00:00:00 2001 From: "Pranev (NeTT)" Date: Mon, 30 Sep 2024 21:03:57 +0530 Subject: [PATCH] chore: clean leftover comments and println + fix shuffle (#69) * remove print from flatten * use stored gates * ignore 0 * start only when cost exists * generate wasm * fix shuffle * update iris example --- crates/core/src/cpu/backend.rs | 2 +- crates/core/src/cpu/layers/embedding.rs | 57 ++++++-- crates/core/src/cpu/layers/flatten.rs | 1 - crates/core/src/cpu/layers/lstm.rs | 125 +++++++++--------- examples/classification/iris.ts | 8 +- .../backends/wasm/lib/netsaur.generated.js | 6 +- .../src/backends/wasm/lib/netsaur_bg.wasm | Bin 955054 -> 1045707 bytes packages/utilities/src/utils/array/split.ts | 37 +++--- packages/utilities/src/utils/misc/matrix.ts | 52 +++++--- 9 files changed, 167 insertions(+), 121 deletions(-) diff --git a/crates/core/src/cpu/backend.rs b/crates/core/src/cpu/backend.rs index 7cdb142..afd13f9 100644 --- a/crates/core/src/cpu/backend.rs +++ b/crates/core/src/cpu/backend.rs @@ -203,7 +203,7 @@ impl Backend { total = 0.0; } } - if self.patience != 0 { + if self.patience != 0 && cost != 0.0 { if best_cost < 0.0 { best_cost = cost; } diff --git a/crates/core/src/cpu/layers/embedding.rs b/crates/core/src/cpu/layers/embedding.rs index 7c34bb6..8e5619e 100644 --- a/crates/core/src/cpu/layers/embedding.rs +++ b/crates/core/src/cpu/layers/embedding.rs @@ -1,5 +1,5 @@ -use std::ops::AddAssign; use ndarray::{Array2, ArrayD, Axis, Ix2, IxDyn}; +use std::ops::AddAssign; use crate::{CPUInit, CPURegularizer, EmbeddingLayer, Init}; @@ -21,7 +21,10 @@ impl EmbeddingCPULayer { pub fn new(config: EmbeddingLayer, size: IxDyn) -> Self { let init = CPUInit::from(Init::Uniform); let output_size = vec![size[0], size[1], config.embedding_size]; - let embeddings = init.init(IxDyn(&[config.vocab_size, config.embedding_size]), 0, 0).into_dimensionality::().unwrap(); + let embeddings = init + .init(IxDyn(&[config.vocab_size, config.embedding_size]), 0, 0) + .into_dimensionality::() + .unwrap(); let d_embeddings = Array2::zeros((config.vocab_size, config.embedding_size)); Self { input_size: size, @@ -32,7 +35,10 @@ impl EmbeddingCPULayer { embeddings, d_embeddings, l_embeddings: Array2::zeros((config.vocab_size, config.embedding_size)), - regularizer: CPURegularizer::from(config.c.unwrap_or(0.0), config.l1_ratio.unwrap_or(1.0)) + regularizer: CPURegularizer::from( + config.c.unwrap_or(0.0), + config.l1_ratio.unwrap_or(1.0), + ), } } @@ -48,21 +54,46 @@ impl EmbeddingCPULayer { let input_indices: Vec = inputs.iter().map(|&x| x as usize).collect(); self.input_indices = input_indices.clone(); let embeddings = self.embeddings.select(Axis(0), input_indices.as_slice()); -// let output_size = IxDyn(&self.output_size); - embeddings.into_shape_with_order(IxDyn(&[inputs.shape()[0], inputs.shape()[1], self.embedding_size])).unwrap() + // let output_size = IxDyn(&self.output_size); + embeddings + .into_shape_with_order(IxDyn(&[ + inputs.shape()[0], + inputs.shape()[1], + self.embedding_size, + ])) + .unwrap() } pub fn backward_propagate(&mut self, d_outputs: ArrayD) -> ArrayD { - let indices = Array2::from_shape_vec(Ix2(d_outputs.shape()[0], self.input_size[1]), self.input_indices.clone()); - self.d_embeddings = Array2::zeros((self.d_embeddings.shape()[0], self.d_embeddings.shape()[1])); - d_outputs.axis_iter(Axis(0)).zip(indices).for_each(|(rec, i)| { - rec.axis_iter(Axis(0)).zip(i).for_each(|(grad, idx)| { - self.d_embeddings.index_axis_mut(Axis(0), idx).add_assign(&grad); + let indices = Array2::from_shape_vec( + Ix2(d_outputs.shape()[0], self.input_size[1]), + self.input_indices.clone(), + ) + .unwrap(); + self.d_embeddings.fill(0.0); + d_outputs + .axis_iter(Axis(0)) + .zip(indices.axis_iter(Axis(0))) + .for_each(|(rec, i)| { + rec.axis_iter(Axis(0)).zip(i).for_each(|(grad, idx)| { + if idx != &0 { + self.d_embeddings + .index_axis_mut(Axis(0), *idx) + .add_assign(&grad); + } + }); }); - }); - self.l_embeddings = self.regularizer.coeff(&self.embeddings.clone().into_dyn()).into_dimensionality::().unwrap(); + self.l_embeddings = self + .regularizer + .coeff(&self.embeddings.clone().into_dyn()) + .into_dimensionality::() + .unwrap(); let mut input_size = self.input_size.clone(); input_size[0] = d_outputs.shape()[0]; - ArrayD::from_shape_vec(input_size, self.input_indices.iter().map(|x| *x as f32).collect()).unwrap() + ArrayD::from_shape_vec( + input_size, + self.input_indices.iter().map(|x| *x as f32).collect(), + ) + .unwrap() } } diff --git a/crates/core/src/cpu/layers/flatten.rs b/crates/core/src/cpu/layers/flatten.rs index 7a6094f..41979b4 100644 --- a/crates/core/src/cpu/layers/flatten.rs +++ b/crates/core/src/cpu/layers/flatten.rs @@ -23,7 +23,6 @@ impl FlattenCPULayer { pub fn forward_propagate(&mut self, inputs: ArrayD) -> ArrayD { let output_size = IxDyn(&[inputs.shape()[0], self.output_size[1]]); - println!("O {:?} {:?}", inputs.shape(), self.output_size); inputs.into_shape_with_order(output_size).unwrap() } diff --git a/crates/core/src/cpu/layers/lstm.rs b/crates/core/src/cpu/layers/lstm.rs index eac1af9..bbfc48e 100644 --- a/crates/core/src/cpu/layers/lstm.rs +++ b/crates/core/src/cpu/layers/lstm.rs @@ -1,8 +1,8 @@ -use crate::{CPUActivation, Activation, CPUInit, CPURegularizer, Init, LSTMLayer, Tensors, LayerNorm}; -use core::f32; -use ndarray::{ - concatenate, s, Array2, Array3, ArrayD, Axis, Dimension, Ix2, Ix3, IxDyn, +use crate::{ + Activation, CPUActivation, CPUInit, CPURegularizer, Init, LSTMLayer, LayerNorm, Tensors, }; +use core::f32; +use ndarray::{concatenate, s, Array2, Array3, ArrayD, Axis, Dimension, Ix2, Ix3, IxDyn}; use std::ops::AddAssign; /// Indices /// 0 - Input Gate @@ -30,12 +30,18 @@ pub struct LSTMCPULayer { pub l_w_hh: Array3, pub l_biases: Array2, - pub h: Array3, - pub c: Array3, + pub i_t: Array3, + pub f_t: Array3, + pub o_t: Array3, + pub g_t: Array3, + + pub h_prev: Array2, + pub c_prev: Array2, pub regularizer: CPURegularizer, } +#[allow(unused_mut)] impl LSTMCPULayer { pub fn new(config: LSTMLayer, size: IxDyn, _tensors: Option) -> Self { let return_sequences = config.return_sequences.unwrap_or(false); @@ -68,14 +74,20 @@ impl LSTMCPULayer { l_w_ih: Array3::zeros(weight_size), l_w_hh: Array3::zeros((4, config.size, config.size)), l_biases: Array2::zeros((4, config.size)), - h: Array3::zeros((size[1], size[0], config.size)), - c: Array3::zeros((size[1], size[0], config.size)), + i_t: Array3::zeros((size[1], size[0], config.size)), + f_t: Array3::zeros((size[1], size[0], config.size)), + o_t: Array3::zeros((size[1], size[0], config.size)), + g_t: Array3::zeros((size[1], size[0], config.size)), + h_prev: Array2::zeros((size[0], config.size)), + c_prev: Array2::zeros((size[0], config.size)), regularizer: CPURegularizer::from( config.c.unwrap_or(0.0), config.l1_ratio.unwrap_or(1.0), ), - activation_h: CPUActivation::from(config.recurrent_activation.unwrap_or(Activation::Sigmoid)), + activation_h: CPUActivation::from( + config.recurrent_activation.unwrap_or(Activation::Sigmoid), + ), activation_o: CPUActivation::from(config.activation.unwrap_or(Activation::Tanh)), } } @@ -92,10 +104,13 @@ impl LSTMCPULayer { pub fn forward_propagate(&mut self, inputs: ArrayD) -> ArrayD { self.inputs = inputs.into_dimensionality::().unwrap(); let output_size = self.w_ih.shape()[2]; - self.h = Array3::zeros((self.inputs.shape()[1], self.inputs.shape()[0], output_size)); - self.c = Array3::zeros((self.inputs.shape()[1], self.inputs.shape()[0], output_size)); - let mut h_t = self.h.index_axis(Axis(0), 0).clone().to_owned(); - let mut c_t = self.c.index_axis(Axis(0), 0).clone().to_owned(); + let mut h_t = Array2::zeros((self.inputs.shape()[0], output_size)); + let mut c_t = Array2::zeros(h_t.raw_dim()); + + self.i_t = Array3::zeros((self.inputs.shape()[1], self.inputs.shape()[0], output_size)); + self.f_t = Array3::zeros(self.i_t.raw_dim()); + self.o_t = Array3::zeros(self.i_t.raw_dim()); + self.g_t = Array3::zeros(self.i_t.raw_dim()); let mut outputs = Array3::zeros(if self.return_sequences { (self.inputs.shape()[0], self.inputs.shape()[1], output_size) @@ -109,7 +124,8 @@ impl LSTMCPULayer { .slice(s![.., t, ..]) .to_owned() .into_dimensionality::() - .unwrap(); // Current input + .unwrap(); + let i_t = (&x_t.dot(&self.w_ih.index_axis(Axis(0), 0)) + &h_t.dot(&self.w_hh.index_axis(Axis(0), 0)) + &self.biases.index_axis(Axis(0), 0)) @@ -127,56 +143,40 @@ impl LSTMCPULayer { + &self.biases.index_axis(Axis(0), 3)) .mapv(|x| (self.activation_o.activate)(&x)); + self.i_t.index_axis_mut(Axis(0), t).assign(&i_t); + self.f_t.index_axis_mut(Axis(0), t).assign(&f_t); + self.o_t.index_axis_mut(Axis(0), t).assign(&o_t); + self.g_t.index_axis_mut(Axis(0), t).assign(&g_t); + c_t = &(&c_t * &f_t) + &(&g_t * &i_t); h_t = &c_t.mapv(|x| (self.activation_o.activate)(&x)) * &o_t; - self.h.index_axis_mut(Axis(0), t).assign(&h_t); - self.c.index_axis_mut(Axis(0), t).assign(&c_t); - if self.return_sequences { outputs.slice_mut(s![.., t, ..]).assign(&h_t); } } + self.h_prev = h_t.clone(); + self.c_prev = c_t.clone(); if self.return_sequences { outputs.into_dyn() - } - else { + } else { h_t.into_dyn() } } - - fn split_gates( - &self, - z: &Array2, - hidden_size: usize, - ) -> (Array2, Array2, Array2, Array2) { - let i_t = z - .slice(ndarray::s![.., ..hidden_size]) - .mapv(|x| (self.activation_h.activate)(&x)); - let f_t = z - .slice(ndarray::s![.., hidden_size..2 * hidden_size]) - .mapv(|x| (self.activation_h.activate)(&x)); - let o_t = z - .slice(ndarray::s![.., 2 * hidden_size..3 * hidden_size]) - .mapv(|x| (self.activation_h.activate)(&x)); - let g_t = z - .slice(ndarray::s![.., 3 * hidden_size..]) - .mapv(|x| (self.activation_o.activate)(&x)); - - (i_t, f_t, o_t, g_t) - } pub fn backward_propagate(&mut self, d_outputs: ArrayD) -> ArrayD { match d_outputs.shape().len() { 2 => { - let d_inputs = self.backward_propagate_2d(d_outputs.into_dimensionality::().unwrap()); + let d_inputs = + self.backward_propagate_2d(d_outputs.into_dimensionality::().unwrap()); d_inputs.into_dyn() } 3 => { - let d_inputs = self.backward_propagate_3d(d_outputs.into_dimensionality::().unwrap()); + let d_inputs = + self.backward_propagate_3d(d_outputs.into_dimensionality::().unwrap()); d_inputs.into_dyn() } - _ => d_outputs + _ => d_outputs, } } pub fn backward_propagate_3d(&mut self, d_outputs: Array3) -> Array3 { @@ -189,8 +189,8 @@ impl LSTMCPULayer { self.d_w_hh = Array3::zeros((4, hidden_size, hidden_size)); self.d_biases = Array2::zeros((4, hidden_size)); - let h_prev = self.h.index_axis(Axis(0), sequence_length - 1); - let c_prev = self.c.index_axis(Axis(0), sequence_length - 1); + let h_prev = self.h_prev.clone(); + let c_prev = self.c_prev.clone(); let mut d_inputs = Array3::::zeros((batch_size, sequence_length, input_size)); @@ -224,27 +224,28 @@ impl LSTMCPULayer { .into_dimensionality::() .unwrap(); - let d_h = d_outputs.slice(s![.., t, ..]).clone().to_owned().into_dimensionality::().unwrap(); + let d_h = d_outputs + .slice(s![.., t, ..]) + .clone() + .to_owned() + .into_dimensionality::() + .unwrap(); d_h_prev = d_h_prev + d_h; -// clip_gradients(&mut d_h_prev, 5f32); - let gates = x_t.dot(&w_ih) - + h_prev.dot(&w_hh) - + &self.biases.to_shape(4 * hidden_size).unwrap(); - let (i_t, f_t, o_t, g_t) = self.split_gates(&gates, hidden_size); + let i_t = self.i_t.index_axis(Axis(0), t); + let f_t = self.f_t.index_axis(Axis(0), t); + let o_t = self.o_t.index_axis(Axis(0), t); + let g_t = self.g_t.index_axis(Axis(0), t); let d_tanned_c = &d_h_prev * &o_t * c_prev.map(|x| (self.activation_o.activate)(&x)); let mut d_c_t = d_tanned_c + &d_c_prev; - // clip_gradients(&mut d_c_t, 5f32); let d_o_t = &d_h_prev * &c_prev.mapv(|x| (self.activation_o.activate)(&x)); let d_f_t = &d_c_t * &c_prev * &f_t.map(|x| (self.activation_h.prime)(x)); let d_i_t = &d_c_t * &g_t * &i_t.map(|x| (self.activation_h.prime)(x)); let d_g_t = &d_c_t * &i_t * &g_t.map(|x| (self.activation_o.prime)(x)); let d_gates = concatenate![Axis(1), d_i_t, d_f_t, d_o_t, d_g_t]; - // println!("ADD {:?}", concatenate![Axis(0), d_i_t, d_f_t, d_o_t, d_g_t].shape()); - // println!("DG {:?} {:?}", d_gates.shape(), &w_ih.shape()); d_inputs .slice_mut(s![.., t, ..]) .assign(&d_gates.dot(&w_ih.t())); @@ -303,8 +304,8 @@ impl LSTMCPULayer { self.d_w_hh = Array3::zeros((4, hidden_size, hidden_size)); self.d_biases = Array2::zeros((4, hidden_size)); - let h_prev = self.h.index_axis(Axis(0), sequence_length - 1); - let c_prev = self.c.index_axis(Axis(0), sequence_length - 1); + let h_prev = self.h_prev.clone(); + let c_prev = self.c_prev.clone(); let mut d_inputs = Array3::::zeros((batch_size, sequence_length, input_size)); @@ -339,14 +340,13 @@ impl LSTMCPULayer { .into_dimensionality::() .unwrap(); - let gates = x_t.dot(&w_ih) - + h_prev.dot(&w_hh) - + &self.biases.to_shape(4 * hidden_size).unwrap(); - let (i_t, f_t, o_t, g_t) = self.split_gates(&gates, hidden_size); + let i_t = self.i_t.index_axis(Axis(0), t); + let f_t = self.f_t.index_axis(Axis(0), t); + let o_t = self.o_t.index_axis(Axis(0), t); + let g_t = self.g_t.index_axis(Axis(0), t); let d_tanned_c = &d_h_prev * &o_t * c_prev.map(|x| (self.activation_o.activate)(&x)); let mut d_c_t = d_tanned_c + &d_c_prev; -// clip_gradients(&mut d_c_t, 5f32); let d_o_t = &d_h_prev * &c_prev.mapv(|x| (self.activation_o.activate)(&x)); let d_f_t = &d_c_t * &c_prev * &f_t.map(|x| (self.activation_h.prime)(x)); @@ -354,7 +354,7 @@ impl LSTMCPULayer { let d_g_t = &d_c_t * &i_t * &g_t.map(|x| (self.activation_o.prime)(x)); let d_gates = concatenate![Axis(1), d_i_t, d_f_t, d_o_t, d_g_t]; - // println!("OT: {:?}\n\nFT {:?}", &d_h_prev, &d_c_prev); + d_inputs .slice_mut(s![.., t, ..]) .assign(&d_gates.dot(&w_ih.t())); @@ -405,6 +405,7 @@ impl LSTMCPULayer { } } +#[allow(dead_code)] fn clip_gradients(grad: &mut Array2, threshold: f32) -> () { let norm = grad.mapv(|x| x.powi(2)).sum().sqrt(); if norm > threshold { diff --git a/examples/classification/iris.ts b/examples/classification/iris.ts index 13e7ffc..e016588 100644 --- a/examples/classification/iris.ts +++ b/examples/classification/iris.ts @@ -55,8 +55,12 @@ const net = new Sequential({ // Define each layer of the network layers: [ - // A dense layer with 16 neurons - DenseLayer({ size: [16] }), + // A dense layer with 8 neurons + DenseLayer({ size: [8] }), + // A ReLu activation layer + ReluLayer(), + // A dense layer with 8 neurons + DenseLayer({ size: [8] }), // A ReLu activation layer ReluLayer(), // A dense layer with 3 neurons diff --git a/packages/core/src/backends/wasm/lib/netsaur.generated.js b/packages/core/src/backends/wasm/lib/netsaur.generated.js index 8c192f3..9565a39 100644 --- a/packages/core/src/backends/wasm/lib/netsaur.generated.js +++ b/packages/core/src/backends/wasm/lib/netsaur.generated.js @@ -4,7 +4,7 @@ // deno-fmt-ignore-file /// -// source-hash: 60e03f192e01f72854ad537d5cc565c02235b170 +// source-hash: 6fae1da844014c6af84634327825fa20fac7ef5f let wasm; let cachedInt32Memory0; @@ -223,10 +223,10 @@ const imports = { __wbindgen_object_drop_ref: function (arg0) { takeObject(arg0); }, - __wbg_log_6f7dfa87fad40a57: function (arg0, arg1) { + __wbg_log_860f982bd7b08de8: function (arg0, arg1) { console.log(getStringFromWasm0(arg0, arg1)); }, - __wbg_now_de5fe0de473bcd7d: typeof Date.now == "function" + __wbg_now_aa5f80cbe756bd45: typeof Date.now == "function" ? 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zwKZqDP?J08$~eb;V>he09>?~fgHHsmP&0l4Zk`=!o>0WA1=#aff(;o&d>Wg7I6bWB z9y@bhcI)$5}g5^@oU6d5bCSxaD z1g$U4DH7}tplbF$tWoRx3R!Og(tB0kh( options: SplitOptions = { ratio: [7, 3], shuffle: false }, ...arr: T @@ -19,31 +20,27 @@ export function useSplit( if (!shuffle) { return [arr.map((x) => x.slice(0, idx)), arr.map((x) => x.slice(idx))] as [ T, - T, + T ]; } else { const shuffled = useShuffle(0, arr[0].length); const x1 = shuffled.slice(0, idx); const x2 = shuffled.slice(idx); return [ - arr.map((x) => - x.filter( - ((_, i, __) => x1.includes(i)) as ( - value: unknown, - index: number, - array: unknown[], - ) => value is typeof x, - ) - ) as typeof arr, - arr.map((x) => - x.filter( - ((_, i, __) => x2.includes(i)) as ( - value: unknown, - index: number, - array: unknown[], - ) => value is typeof x, - ) - ) as typeof arr, + arr.map((x) => { + if (x instanceof Matrix) { + return x.rowSelect(x1); + } else { + return x1.map((idx) => (x as unknown[])[idx]); + } + }) as unknown as typeof arr, + arr.map((x) => { + if (x instanceof Matrix) { + return x.rowSelect(x2); + } else { + return x2.map((idx) => (x as unknown[])[idx]); + } + }) as unknown as typeof arr, ]; } } diff --git a/packages/utilities/src/utils/misc/matrix.ts b/packages/utilities/src/utils/misc/matrix.ts index 66ec500..5cae15e 100644 --- a/packages/utilities/src/utils/misc/matrix.ts +++ b/packages/utilities/src/utils/misc/matrix.ts @@ -27,8 +27,10 @@ export type MatrixLike

= { * This is a collection of row vectors. * A special case of Tensor for 2D data. */ -export class Matrix
extends Tensor - implements Sliceable, MatrixLike
{ +export class Matrix
+ extends Tensor + implements Sliceable, MatrixLike
+{ /** * Create a matrix from a typed array * @param data Data to move into the matrix. @@ -40,7 +42,7 @@ export class Matrix
extends Tensor constructor(dType: DT, shape: Shape<2>); constructor( data: NDArray
[2] | DType
| DT | TensorLike, - shape?: Shape<2> | DT, + shape?: Shape<2> | DT ) { // @ts-ignore This call will work super(data, shape); @@ -84,16 +86,15 @@ export class Matrix
extends Tensor } /** Get the transpose of the matrix. This method clones the matrix. */ get T(): Matrix
{ - const resArr = new (this.data.constructor as DTypeConstructor
)( - this.nRows * this.nCols, - ) as DType
; + const res = new Matrix(this.dType, + [this.nCols, this.nRows] + ); let i = 0; for (const col of this.cols()) { - // @ts-ignore This line will work - resArr.set(col, i * this.nRows); + res.setRow(i, col); i += 1; } - return new Matrix(resArr, this.shape); + return res; } /** Get a pretty version for printing. DO NOT USE FOR MATRICES WITH MANY COLUMNS. */ get pretty(): string { @@ -105,14 +106,14 @@ export class Matrix
extends Tensor return res; } /** Alias for row */ - at(pos: number): DType
{ - return this.row(pos); + at(pos: number): Matrix
{ + return new Matrix(this.row(pos), [1, this.nCols]); } /** Get the nth column in the matrix */ col(n: number): DType
{ let i = 0; const col = new (this.data.constructor as DTypeConstructor
)( - this.nRows, + this.nRows ) as DType
; let offset = 0; while (i < this.nRows) { @@ -134,10 +135,14 @@ export class Matrix
extends Tensor } return sum; } + /** Return a new matrix with only the selected rows in the order of supplied indices. */ + colSelect(cols: number[]): Matrix
{ + return this.T.rowSelect(cols).T; + } /** Get a column array of all column sums in the matrix */ colSum(): DType
{ const sum = new (this.data.constructor as DTypeConstructor
)( - this.nRows, + this.nRows ) as DType
; let i = 0; while (i < this.nCols) { @@ -167,7 +172,8 @@ export class Matrix
extends Tensor while (j < this.nCols) { let i = 0; while (i < this.nRows) { - const adder = (this.item(i, j) as DTypeValue
) * + const adder = + (this.item(i, j) as DTypeValue
) * (rhs.item(i, j) as DTypeValue
); // @ts-ignore I'll fix this later res += adder as DTypeValue
; @@ -179,7 +185,7 @@ export class Matrix
extends Tensor } /** Filter the matrix by rows */ override filter( - fn: (value: DType
, row: number, _: DType
[]) => boolean, + fn: (value: DType
, row: number, _: DType
[]) => boolean ): Matrix
{ const satisfying: number[] = []; let i = 0; @@ -218,10 +224,18 @@ export class Matrix
extends Tensor } return sum; } + /** Return a new matrix with only the selected rows in the order of supplied indices. */ + rowSelect(rows: number[]): Matrix
{ + const res = new Matrix(this.dType, [rows.length, this.nCols]); + for (let i = 0; i < rows.length; i += 1) { + res.setRow(i, this.row(rows[i])); + } + return res; + } /** Compute the sum of all rows */ rowSum(): DType
{ const sum = new (this.data.constructor as DTypeConstructor
)( - this.nCols, + this.nCols ) as DType
; let i = 0; let offset = 0; @@ -268,9 +282,9 @@ export class Matrix
extends Tensor return new Matrix
( this.data.slice( start ? start * this.nCols : 0, - end ? end * this.nCols : undefined, + end ? end * this.nCols : undefined ) as DType
, - [end ? end - start : this.nRows - start, this.nCols], + [end ? end - start : this.nRows - start, this.nCols] ); } /** Iterate through rows */ @@ -287,7 +301,7 @@ export class Matrix
extends Tensor while (i < this.nCols) { let j = 0; const col = new (this.data.constructor as DTypeConstructor
)( - this.nRows, + this.nRows ) as DType
; while (j < this.nRows) { col[j] = this.data[j * this.nCols + i];