chore: jsr deployment

README.md

@@ -55,7 +55,8 @@
 ### QuickStart
 
 This example shows how to train a neural network to predict the output of the
-XOR function our speedy CPU backend written in [Rust](https://www.rust-lang.org/).
+XOR function our speedy CPU backend written in
+[Rust](https://www.rust-lang.org/).
 
 ```typescript
 import {
@@ -253,7 +254,7 @@ console.log(`1 xor 1 = ${out4[0]} (should be close to 0)`);
 ### Documentation
 
 The full documentation for Netsaur can be found
-[here](https://deno.land/x/[email protected].0/mod.ts).
+[here](https://deno.land/x/[email protected].1/mod.ts).
 
 ### License
 

crates/README.md

@@ -4,6 +4,8 @@ This directory contains the source code for the Netsaur Rust crates.
 
 ## Crates
 
-* [core](/core) - The main crate for the Netsaur FFI and wasm bindings.
-* [core-gpu](/core-gpu) - The main crate for the Netsaur GPU FFI and wasm bindings.
-* [tokenizers](/tokenizers) - The main crate for the Netsaur tokenizers wasm bindings.
+- [core](/core) - The main crate for the Netsaur FFI and wasm bindings.
+- [core-gpu](/core-gpu) - The main crate for the Netsaur GPU FFI and wasm
+  bindings.
+- [tokenizers](/tokenizers) - The main crate for the Netsaur tokenizers wasm
+  bindings.

crates/core-gpu/Cargo.toml

@@ -1,7 +1,7 @@
 [package]
 edition = "2021"
 name = "netsaur-gpu"
-version = "0.3.0"
+version = "0.3.1"
 
 [lib]
 crate-type = ["cdylib"]

crates/core/Cargo.toml

@@ -1,7 +1,7 @@
 [package]
 edition = "2021"
 name = "netsaur"
-version = "0.3.0"
+version = "0.3.1"
 
 [lib]
 crate-type = ["cdylib"]

crates/tokenizers/Cargo.toml

@@ -1,7 +1,7 @@
 [package]
 edition = "2021"
 name = "netsaur-tokenizers"
-version = "0.3.0"
+version = "0.3.1"
 
 [lib]
 crate-type = ["cdylib"]

data/data.ts

@@ -21,7 +21,7 @@ export class Data {
   /**
    * Load data from a CSV file or URL containing CSV data.
    */
-  static async csv(url: string | URL, config?: CsvLoaderConfig) {
+  static async csv(url: string | URL, config?: CsvLoaderConfig): Promise<Data> {
     return new Data(await loadCsv(url, config));
   }
 }

data/datasets/csv.ts

@@ -33,7 +33,7 @@ export async function loadCsv(
   let columnIndices!: Record<string, number>;
   for await (const row of data) {
     if (!columnNames) {
-      columnNames = row;
+      columnNames = row as string[];
       columnIndices = columnNames.reduce((acc, col, i) => {
         acc[col] = i;
         return acc;
@@ -45,7 +45,7 @@ export async function loadCsv(
     for (const col in columnIndices) {
       const colConfig = config.columns?.[col];
       const i = columnIndices[col];
-      const value = row[i];
+      const value = (row as string[])[i];
       if (colConfig?.label) {
         y.push(Number(value));
       } else {

data/deps.ts

@@ -1 +1 @@
-export { CsvParseStream } from "https://deno.land/[email protected]/csv/csv_parse_stream.ts";
+export { CsvParseStream } from "jsr:@std/csv@0.214.0";

deno.json

@@ -1,4 +1,11 @@
 {
+  "name": "@denosaurs/netsaur",
+  "version": "0.3.1",
+  "exports": {
+    ".": "./mod.ts",
+    "./tokenizers": "./tokenizers/mod.ts",
+    "./data": "./data/mod.ts"
+  },
   "tasks": {
     "example:xor": "deno run -A --unstable-ffi ./examples/xor_auto.ts",
     "example:xor-option": "deno run -A --unstable-ffi ./examples/xor_option.ts",

deps.ts

@@ -1,2 +1,2 @@
-export { dlopen } from "https://deno.land/x/[email protected]/mod.ts";
-export type { FetchOptions } from "https://deno.land/x/[email protected]/mod.ts";
+export { dlopen } from "jsr:@denosaurs/[email protected]";
+export type { FetchOptions } from "jsr:@denosaurs/[email protected]";

examples/classification/README.md

@@ -1,3 +1,4 @@
 # Binary Classification
-This example showcases binary classification on the Iris dataset. 
-The `Iris Virginica` class is omitted for this example.
+
+This example showcases binary classification on the Iris dataset. The
+`Iris Virginica` class is omitted for this example.

examples/classification/binary_iris.ts

@@ -75,7 +75,7 @@ net.train(
   150,
   1,
   // Use a smaller learning rate
-  0.02
+  0.02,
 );
 
 console.log(`training time: ${performance.now() - time}ms`);

examples/classification/iris.ts

@@ -16,12 +16,12 @@ import { parse } from "https://deno.land/[email protected]/csv/parse.ts";
 
 // Import helpers for metrics
 import {
-  ClassificationReport,
-  // Split the dataset
-  useSplit,
   // One-hot encoding of targets
   CategoricalEncoder,
+  ClassificationReport,
   Matrix,
+  // Split the dataset
+  useSplit,
 } from "https://deno.land/x/[email protected]/mod.ts";
 
 // Read the training dataset
@@ -40,7 +40,7 @@ const y = encoder.fit(y_pre).transform(y_pre, "f32");
 // @ts-ignore Matrices can be split
 const [train, test] = useSplit({ ratio: [7, 3], shuffle: true }, x, y) as [
   [typeof x, typeof y],
-  [typeof x, typeof y]
+  [typeof x, typeof y],
 ];
 
 // Setup the CPU backend for Netsaur
@@ -87,7 +87,7 @@ net.train(
   // Train for 300 epochs
   400,
   1,
-  0.02
+  0.02,
 );
 
 console.log(`training time: ${performance.now() - time}ms`);
@@ -96,8 +96,8 @@ console.log(`training time: ${performance.now() - time}ms`);
 const res = await net.predict(tensor2D(test[0]));
 const y1 = encoder.untransform(
   CategoricalEncoder.fromSoftmax(
-    new Matrix(res.data, [res.shape[0], res.shape[1]])
-  )
+    new Matrix(res.data, [res.shape[0], res.shape[1]]),
+  ),
 );
 const y0 = encoder.untransform(test[1]);
 
@@ -106,5 +106,5 @@ const cMatrix = new ClassificationReport(y0, y1);
 console.log(cMatrix);
 console.log(
   "Total Accuracy: ",
-  y1.filter((x, i) => x === y0[i]).length / y1.length
+  y1.filter((x, i) => x === y0[i]).length / y1.length,
 );

examples/classification/spam.ts

@@ -35,7 +35,7 @@ const y = data.map((msg) => ymap.indexOf(msg[0]));
 // Split the dataset for training and testing
 const [train, test] = useSplit({ ratio: [7, 3], shuffle: true }, x, y) as [
   [typeof x, typeof y],
-  [typeof x, typeof y]
+  [typeof x, typeof y],
 ];
 
 // Vectorize the text messages
@@ -88,7 +88,7 @@ net.train(
   // Train for 20 epochs
   20,
   2,
-  0.01
+  0.01,
 );
 
 console.log(`training time: ${performance.now() - time}ms`);
@@ -97,7 +97,6 @@ const x_vec_test = vec.transform(test[0]);
 
 // Calculate metrics
 const res = await net.predict(tensor(x_vec_test.data, x_vec_test.shape));
-const y1 = res.data.map(i => i < 0.5 ? 0 : 1)
+const y1 = res.data.map((i) => i < 0.5 ? 0 : 1);
 const cMatrix = new ClassificationReport(test[1], y1);
 console.log("Confusion Matrix: ", cMatrix);
-

examples/linear.ts

@@ -70,7 +70,7 @@ network.train(
   /**
    * The learning rate is set to 0.01.
    */
-  0.01
+  0.01,
 );
 
 console.log("training time", performance.now() - start, " milliseconds");
@@ -84,6 +84,6 @@ for (const [i, res] of predicted.data.entries()) {
   console.log(
     `input: ${testData[i]}\noutput: ${res.toFixed(2)}\nexpected: ${
       2 * testData[i] + 1
-    }\n`
+    }\n`,
   );
 }

examples/mnist/predict.ts

@@ -32,8 +32,8 @@ let correct = 0;
 for (const test of testSet) {
   const prediction = argmax(
     await network.predict(
-      tensor(test.inputs.data, [1, ...test.inputs.shape] as Shape[keyof Shape])
-    )
+      tensor(test.inputs.data, [1, ...test.inputs.shape] as Shape[keyof Shape]),
+    ),
   );
   const expected = argmax(test.outputs as Tensor<Rank>);
   if (expected === prediction) {

examples/mnist/train_batchnorm.ts

@@ -55,4 +55,4 @@ const start = performance.now();
 network.train(trainSet, epochs, 1, 0.01);
 console.log("Training complete!", performance.now() - start);
 
-network.saveFile("examples/mnist/mnist.test.st");
+network.saveFile("examples/mnist/mnist.test.st");

examples/visualize.ipynb

@@ -63,19 +63,19 @@
     }
    ],
    "source": [
-    "import { tensor1D } from \"https://deno.land/x/[email protected].0/mod.ts\";\n",
-    "import { Visualizer } from \"https://deno.land/x/[email protected].0/visualizer/mod.ts\";\n",
+    "import { tensor1D } from \"https://deno.land/x/[email protected].1/mod.ts\";\n",
+    "import { Visualizer } from \"https://deno.land/x/[email protected].1/visualizer/mod.ts\";\n",
     "\n",
     "import {\n",
-    "  Cost,\n",
     "  AUTO,\n",
+    "  Cost,\n",
     "  DenseLayer,\n",
     "  Sequential,\n",
     "  setupBackend,\n",
     "  SigmoidLayer,\n",
     "  tensor2D,\n",
-    "} from \"https://deno.land/x/[email protected].0/mod.ts\";\n",
-    "  \n",
+    "} from \"https://deno.land/x/[email protected].1/mod.ts\";\n",
+    "\n",
     "await setupBackend(AUTO);\n",
     "\n",
     "const net = new Sequential({\n",
@@ -107,10 +107,9 @@
     "  ],\n",
     "  1000000,\n",
     ");\n",
-    "  \n",
+    "\n",
     "const visualizer = new Visualizer(\"XOR Example\");\n",
-    "await visualizer.graph(net,\n",
-    "  [\n",
+    "await visualizer.graph(net, [\n",
     "  tensor1D([0, 0]),\n",
     "  tensor1D([1, 0]),\n",
     "  tensor1D([0, 1]),\n",
@@ -119,8 +118,8 @@
     "  tensor1D([0]),\n",
     "  tensor1D([1]),\n",
     "  tensor1D([1]),\n",
-    "  tensor1D([0])\n",
-    "])"
+    "  tensor1D([0]),\n",
+    "]);"
    ]
   }
  ],