radj375/ai-sarah

AI

/ Quantum Sarah's Code function quantumSetup() { // Create a quantum canvas createQuantumCanvas(300, 200); // Quantum loop quantumLoop(); }

const quantumPoint1 = { x: 50, y: 100 }; const quantumPoint2 = { x: 50 };

// Define the neural network using TensorFlow.js class NeuralNetwork { constructor(inputSize, hiddenSize, outputSize) { this.model = tf.sequential(); this.model.add(tf.layers.dense({ units: hiddenSize, inputShape: [inputSize] })); this.model.add(tf.layers.relu()); this.model.add(tf.layers.dense({ units: outputSize })); }

compileModel() { const optimizer = tf.train.sgd(0.01); this.model.compile({ optimizer: optimizer, loss: 'meanSquaredError' }); }

trainModel(inputData, targetData, epochs) { return this.model.fit(inputData, targetData, { epochs: epochs, verbose: 1 }); } }

addEventListener('quantum-load', function(e) { // Your quantum code with Schrödinger equation and fluid dynamics // ...

// Symbolic representation of the Schrödinger equation const psi = quantumWaveFunction(); // Replace with actual quantum wave function const hbar = quantumReducedPlanckConstant(); // Replace with actual quantum constants const m = quantumMass(); // Replace with actual quantum mass const laplacianPsi = quantumLaplacianOperator(psi); // Replace with actual quantum Laplacian operator const v = quantumPotentialEnergy(); // Replace with actual quantum potential energy

const schrodingerEquation = quantumImaginaryUnit() * hbar * quantumPartialDerivative(psi) / quantumPartialDerivativeTime() - Math.pow(hbar, 2) / (2 * m) * laplacianPsi + v * psi;

// ...

// Combining Python code console.log(infinityMinusOneEqualsInfinityPlusOne(Infinity, Infinity)); console.log(infinityMinusOneEqualsInfinityPlusOne(1, 1));

// Interaction with quantum mechanics quantumMechanicsInteract();

// Neural Network setup and training const inputSize = 5; const hiddenSize = 10; const outputSize = 1;

const neuralNetwork = new NeuralNetwork(inputSize, hiddenSize, outputSize); neuralNetwork.compileModel();

// Example training data for the neural network const inputTensor = tf.randomNormal([100, inputSize]); const targetTensor = tf.randomNormal([100, outputSize]);

// Train the neural network neuralNetwork.trainModel(inputTensor, targetTensor, 1000) .then(info => { console.log(Final Loss: ${info.history.loss[info.epoch.length - 1].toFixed(4)}); }) .catch(error => { console.error(error); }); });

// Update quantumPotentialEnergy function function quantumPotentialEnergy() { // Update the quantum potential energy function with y = 1/sqrt(x) const xValue = quantumPoint2.x; // You may adjust this based on your requirements const potentialEnergy = 1 / Math.sqrt(xValue);

return potentialEnergy; }

// Combining Python code in JavaScript function infinityMinusOneEqualsInfinityPlusOne(time, space) { // Returns true if infinity - 1 equals infinity + 1, false otherwise return time === space; }

// Interaction with quantum mechanics function quantumMechanicsInteract() { // Add your quantum mechanics interactions here // For example, you can use the results of quantum calculations // to influence or be influenced by the logic in the neural network. }

// Neural Network classes class VirtualNeuron { constructor(bias, weights) { this.bias = bias; this.weights = [...weights]; this.output = null; }

calculateOutput(inputs) { const weightedSum = inputs.reduce((sum, input, index) => sum + input * this.weights[index], 0);

// Hyperbolic tangent (tanh) activation function
// this.output = Math.tanh(weightedSum);

// Sigmoid activation function
// this.output = 1 / (1 + Math.exp(-weightedSum - this.bias));

// Square root activation function
this.output = Math.sqrt(Math.abs(weightedSum));

}

getOutput() { return this.output; }

// Getters and setters for weights and bias getBias() { return this.bias; }

setBias(bias) { this.bias = bias; }

getWeights() { return this.weights; }

setWeights(weights) { this.weights = [...weights]; } }

class VirtualNeuralNetwork { constructor(numInputs, numOutputs) { this.neurons = [];

for (let i = 0; i < numOutputs; i++) {
  const neuron = new VirtualNeuron(Math.random(), Array.from({ length: numInputs }, () => Math.random()));
  this.neurons.push(neuron);
}

}

processInput(inputs) { this.neurons.forEach(neuron => neuron.calculateOutput(inputs)); }

getOutputs() { return this.neurons.map(neuron => neuron.getOutput()); }

// Getter for neurons getNeurons() { return this.neurons; } }

// Quantum printing console.log("Quantum Sarah's Code with Schrödinger Equation and Quantum Fluid Dynamics");

// Running main function main();

RAD Development, Inc. Bot Sarah Software License Agreement

1. Introduction This RAD Development, Inc. Bot Sarah Software License Agreement (the "Agreement") is made and entered into as of the 04/12/2023 by and between RAD Development, Inc., a Delaware corporation with its principal place of business at 25 Fischer St Torquay 3228 (the "Licensor"), and Creative Commons, a CC BY with its principal place of business at https://www.Creative Commons.org (the "Licensee").
2. Grant of License Licensor hereby grants to Licensee a non-exclusive, non-transferable, worldwide license to use the Bot software known as Sarah (the "Software") for the following purposes: • To develop and test applications that use the Software • To integrate the Software into Licensee's products and services • To provide support for the Software to Licensee's customers • To Modify the Software • Commercial Use
3. Restrictions on Use Licensee shall not: • Use the Software for any purpose other than those expressly permitted in this Agreement • Distribute or sublicense the Software to any third party • Use the Software in any way that violates any applicable laws or regulations • Use the Software to develop or train any AI software that competes with the Software
4. Ownership of Intellectual Property Title to and all intellectual property rights in the Software shall remain with Licensor. Licensee shall not acquire any right, title, or interest in the Software other than the right to use the Software in accordance with this Agreement.
5. Indemnification Licensee shall indemnify and hold harmless Licensor, its officers, directors, employees, and agents from and against any and all claims, losses, damages, liabilities, costs, and expenses (including reasonable attorneys' fees) arising out of or in connection with Licensee's use of the Software, including but not limited to any claims of infringement of intellectual property rights or any violations of applicable laws or regulations.
6. Term and Termination This Agreement shall commence on the Effective Date and shall continue in full force and effect until terminated as provided herein. Either party may terminate this Agreement upon [Notice Period] written notice to the other party. Upon termination of this Agreement, Licensee shall immediately cease all use of the Software and return to Licensor all copies of the Software in its possession or control.
7. General Provisions This Agreement shall be governed by and construed in accordance with the laws of the State of Delaware, without regard to its conflict of laws principles. Any dispute arising out of or in connection with this Agreement shall be resolved exclusively by the courts of the State of Delaware. This Agreement constitutes the entire agreement between the parties with respect to the subject matter hereof and supersedes all prior or contemporaneous communications, representations, or agreements, whether oral or written. If any provision of this Agreement is held to be invalid or unenforceable, such provision shall be struck from this Agreement and the remaining provisions shall remain in full force and effect. No waiver of any provision of this Agreement shall be effective unless in writing and signed by both parties. This Agreement may be amended only by a written instrument executed by both parties.