updated .gitignore, added documentation, fixed typo in example comment

.gitignore

@@ -1,2 +1,4 @@
 Prover.toml
-Verifier.toml
+Verifier.toml
+proofs
+lib.nr

example.py

@@ -17,7 +17,7 @@
 mlp = MLPClassifier((2,3), activation='relu', max_iter=2000)
 mlp.fit(X, y)
 
-# Generate proof for thr first row
+# Generate proof for the first row
 mlpcp = MLPClassifierProver(
     mlp,
     'src/main.nr',

proofs/mlp-proof.proof

@@ -1 +1 @@
-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

readme.md

@@ -0,0 +1,53 @@
+# SKProof
+## About
+SKProof [Python](https://python.org/) library enables generation of execution proofs for machine learning models found in [scikit-learn](https://scikit-learn.org/) library. Current version enable generation of proofs for multilayer perceptron neural network classifier ([MLPClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html)) but the following updates will include support for more models. The circuits are designed using [Noir](https://noir-lang.org/) language and the PLONK proofs are generated internally using [Nargo](https://noir-lang.org/getting_started/nargo.html) CLI tool, also used to generate Solidity smart contract verifiers for the models. For representing floating point values, we use our [ZKFloat](https://github.com/0x3327/zkfloat) library, written in [Noir](https://noir-lang.org/) language for handling base 10 floating point values.
+
+### Supported models
+- MLPClassifier with ReLU activation function
+
+## How it works
+The first step is ML model training, where the model parameters are determined. In case of [MLPClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html), the model parameters are weights between network nodes. The trained model is decomposed into simple addition and multiplication expression, as well as activation function calls. All expressions are converted into calls of [ZKFloat](https://github.com/0x3327/zkfloat) library methods for arithmetic operations over floating point numbers and written, along with the library code, into [Noir](https://noir-lang.org/) program. A user can select the instance for which the prediction proof should be generated and requests proof generation. The SKProof prover invokes [Nargo](https://noir-lang.org/getting_started/nargo.html) CLI commands to compile the circuit and generate the proof for the prediction.
+
+## Planned improvements
+- Code optimization
+- Support for more models
+
+## Prerequisites
+The library uses [Noir](https://noir-lang.org/) code to generate proofs for the [scikit-learn](https://scikit-learn.org/) models, so it is a requirement that you have installed:
+- scikit-learn library; install using pip with command `pip install sklearn`
+- noir library; Installation instructions can be found [here](https://noir-lang.org/getting_started/nargo/installation.html)
+
+## Installation
+Installing skproof package is done using pip with command `pip install skproof`
+
+## Example
+Example using Iris dataset and MLPClassifier
+```python
+from skproof.mlp.MLPClassifierProver import MLPClassifierProver
+from sklearn.neural_network import MLPClassifier
+from sklearn.datasets import load_iris
+
+print('Loading dataset...')
+
+# Load test data
+iris = load_iris()
+X = iris.data
+y = iris.target
+
+print('Training MLPClassifier...')
+
+# Train classifier
+mlp = MLPClassifier((2,3), activation='relu', max_iter=2000)
+mlp.fit(X, y)
+
+# Generate proof for the first row
+mlpcp = MLPClassifierProver(
+    mlp,
+    'src/main.nr',
+    'Prover.toml',
+    '../zkfloat/zkfloat.nr',
+    7
+)
+
+mlpcp.prove(X[:1,:])
+```