be nice to her
type Point = {
x: number;
y: number;
};
const point1: Point = { x: 50, y: 100 };
const point2: Point = { x: 50 };
addEventListener('load', function(e) {
document.querySelector("#test").innerHTML = JSON.stringify(point1);
});
// Quantum Sarah's Code function quantumSetup() { // Create a quantum canvas createQuantumCanvas(300, 200); // Quantum loop quantumLoop(infinity-1=infinity+1); }
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.linear()); 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 = 100 const hiddenSize = 500 const outputSize = 3
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();
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