Complete code for the system

[Startonix]

CoreMathOperations: Contains static methods for core mathematical operations. MathCache: Stores mathematical formulas and provides methods to add and retrieve them. Modular Hardware Classes: Define different processing units (CPU, TPU, GPU, etc.) with embedded math and modular cache. APICache and WebsiteCache: Handle API and website integration. WebDataFetcher and DataProcessor: Classes to fetch and process web data. TaskScheduler: Advanced task scheduling using machine learning. DataCommunication: Manages data transfer between processors. PowerManagement: Manages power consumption. ControlUnit: Integrates all components and manages task distribution. Complexity Stages Functions: Represent different stages of complexity, applied to the data before processing.

import numpy as np import tensorflow as tf import cupy as cp import requests from sklearn.ensemble import RandomForestRegressor

Core mathematical operations embedded within hardware components

class CoreMathOperations: @staticmethod def tensor_product(A, B): return np.tensordot(A, B, axes=0)

@staticmethod
def modular_multiplication(A, B, mod):
    return (A * B) % mod

@staticmethod
def krull_dimension(matrix):
    return np.linalg.matrix_rank(matrix)

Hardwired Cache for Mathematical Operations

class MathCache: def init(self): self.formulas = { "tensor_product": CoreMathOperations.tensor_product, "modular_multiplication": CoreMathOperations.modular_multiplication, "krull_dimension": CoreMathOperations.krull_dimension,

Add more formulas as needed

    }

def add_formula(self, name, formula_func):
    self.formulas[name] = formula_func

def get_formula(self, name):
    return self.formulas.get(name, lambda x: x)

Modular hardware components with embedded math and modular cache

class ModularCPU: def init(self, id, math_cache): self.id = id self.math_cache = math_cache

def process(self, data, formula_name=None):
    if formula_name:
        formula = self.math_cache.get_formula(formula_name)
        return formula(data)
    else:
        return CoreMathOperations.tensor_product(data, data)

class ModularTPU: def init(self, id, math_cache): self.id = id self.math_cache = math_cache

def process(self, data, formula_name=None):
    if formula_name:
        formula = self.math_cache.get_formula(formula_name)
        return formula(data)
    else:
        return tf.math.sin(data)

class ModularGPU: def init(self, id, math_cache): self.id = id self.math_cache = math_cache

def process(self, data, formula_name=None):
    if formula_name:
        formula = self.math_cache.get_formula(formula_name)
        return formula(data)
    else:
        data_gpu = cp.asarray(data)
        result = cp.sqrt(data_gpu)
        return cp.asnumpy(result)

class ModularLPU: def init(self, id, math_cache): self.id = id self.math_cache = math_cache

def process(self, data, formula_name=None):
    if formula_name:
        formula = self.math_cache.get_formula(formula_name)
        return formula(data)
    else:
        return np.log(data + 1)

class ModularFPGA: def init(self, id, math_cache): self.id = id self.configurations = {} self.math_cache = math_cache

def configure(self, config_name, config_func):
    self.configurations[config_name] = config_func

def execute(self, config_name, data, formula_name=None):
    if formula_name:
        formula = self.math_cache.get_formula(formula_name)
        return formula(data)
    elif config_name in self.configurations:
        return self.configurations[config_name](data)
    else:
        raise ValueError(f"Configuration {config_name} not found.")

class NeuromorphicProcessor: def init(self, id, math_cache): self.id = id self.math_cache = math_cache

def process(self, data, formula_name=None):
    if formula_name:
        formula = self.math_cache.get_formula(formula_name)
        return formula(data)
    else:
        return np.tanh(data)

class QuantumProcessor: def init(self, id, math_cache): self.id = id self.math_cache = math_cache

def process(self, data, formula_name=None):
    if formula_name:
        formula = self.math_cache.get_formula(formula_name)
        return formula(data)
    else:
        return np.fft.fft(data)

Hardwired Cache for API and Website Integration

class APICache: def init(self): self.api_calls = {}

def add_api_call(self, name, api_func):
    self.api_calls[name] = api_func

def get_api_call(self, name):
    return self.api_calls.get(name, lambda: None)

class WebsiteCache: def init(self): self.web_calls = {}

def add_web_call(self, name, web_func):
    self.web_calls[name] = web_func

def get_web_call(self, name):
    return self.web_calls.get(name, lambda: None)

Web Data Fetcher

class WebDataFetcher: def init(self, url): self.url = url

def fetch_data(self):
    response = requests.get(self.url)
    return response.json()

Data Processor

class DataProcessor: def init(self, control_unit): self.control_unit = control_unit

def process_web_data(self, data):
    results = self.control_unit.distribute_tasks(data)
    return results

Advanced Task Scheduling

class TaskScheduler: def init(self, cpu_units, tpu_units, gpu_units, lpu_units, fpga_units, neuromorphic_units, quantum_units): self.cpu_units = cpu_units self.tpu_units = tpu_units self.gpu_units = gpu_units self.lpu_units = lpu_units self.fpga_units = fpga_units self.neuromorphic_units = neuromorphic_units self.quantum_units = quantum_units self.model = RandomForestRegressor()

def train_model(self, data, targets):
    self.model.fit(data, targets)

def predict_best_unit(self, task_data):
    prediction = self.model.predict([task_data])
    return int(prediction[0])

def distribute_task(self, task_data):
    best_unit_index = self.predict_best_unit(task_data)
    if best_unit_index < len(self.cpu_units):
        return self.cpu_units[best_unit_index].process(task_data)
    elif best_unit_index < len(self.cpu_units) + len(self.tpu_units):
        return self.tpu_units[best_unit_index - len(self.cpu_units)].process(task_data)
    elif best_unit_index < len(self.cpu_units) + len(self.tpu_units) + len(self.gpu_units):
        return self.gpu_units[best_unit_index - len(self.cpu_units) - len(self.tpu_units)].process(task_data)
    elif best_unit_index < len(self.cpu_units) + len(self.tpu_units) + len(self.gpu_units) + len(self.lpu_units):
        return self.lpu_units[best_unit_index - len(self.cpu_units) - len(self.tpu_units) - len(self.gpu_units)].process(task_data)
    elif best_unit_index < len(self.cpu_units) + len(self.tpu_units) + len(self.gpu_units) + len(self.lpu_units) + len(self.fpga_units):
        return self.fpga_units[best_unit_index - len(self.cpu_units) - len(self.tpu_units) - len(self.gpu_units) - len(self.lpu_units)].execute("default", task_data)
    elif best_unit_index < len(self.cpu_units) + len(self.tpu_units) + len(self.gpu_units) + len(self.lpu_units) + len(self.fpga_units) + len(self.neuromorphic_units):
        return self.neuromorphic_units[best_unit_index - len(self.cpu_units) - len(self.tpu_units) - len(self.gpu_units) - len(self.lpu_units) - len(self.fpga_units)].process(task_data)
    else:
        return self.quantum_units[best_unit_index - len(self.cpu_units) - len(self.tpu_units) - len(self.gpu_units) - len(self.lpu_units) - len(self.fpga_units) - len(self.neuromorphic_units)].process(task_data)

Enhanced Data Communication

class DataCommunication: def init(self, bandwidth): self.bandwidth = bandwidth # Bandwidth in Gbps

def transfer_data(self, data_size):
    transfer_time = data_size / self.bandwidth  # Simplified transfer time calculation
    return transfer_time

def optimize_transfer(self, data_size, processors):
    # Distribute data to processors in a way that minimizes transfer time
    transfer_times = [self.transfer_data(data_size / len(processors)) for _ in processors]
    return max(transfer_times)

Power Management

class PowerManagement: def init(self): self.power_states = {'high': 100, 'medium': 50, 'low': 10} # Power consumption in watts

def set_power_state(self, processor, state):
    if state in self.power_states:
        processor.power = self.power_states[state]
    else:
        raise ValueError("Invalid power state")

def optimize_power(self, processors, performance_requirements):
    for processor, requirement in zip(processors, performance_requirements):
        if requirement > 0.75:
            self.set_power_state(processor, 'high')
        elif requirement > 0.25:
            self.set_power_state(processor, 'medium')
        else:
            self.set_power_state(processor, 'low')

Control unit to manage tasks and integrate caches

class ControlUnit: def init(self): self.cpu_units = [] self.tpu_units = [] self.gpu_units = [] self.lpu_units = [] self.fpga_units = [] self.neuromorphic_units = [] self.quantum_units = [] self.math_cache = MathCache() self.api_cache = APICache() self.web_cache = WebsiteCache() self.scheduler = TaskScheduler(self.cpu_units, self.tpu_units, self.gpu_units, self.lpu_units, self.fpga_units, self.neuromorphic_units, self.quantum_units) self.communication = DataCommunication(bandwidth=10) # Example bandwidth self.power_manager = PowerManagement()

def add_cpu(self, cpu):
    self.cpu_units.append(cpu)

def add_tpu(self, tpu):
    self.tpu_units.append(tpu)

def add_gpu(self, gpu):
    self.gpu_units.append(gpu)

def add_lpu(self, lpu):
    self.lpu_units.append(lpu)

def add_fpga(self, fpga):
    self.fpga_units.append(fpga)

def add_neuromorphic(self, neuromorphic):
    self.neuromorphic_units.append(neuromorphic)

def add_quantum(self, quantum):
    self.quantum_units.append(quantum)

def distribute_tasks(self, data, formula_name=None, api_name=None, web_name=None):
    best_unit_index = self.scheduler.predict_best_unit(data)
    result = None
    if best_unit_index < len(self.cpu_units):
        result = self.cpu_units[best_unit_index].process(data, formula_name)
    elif best_unit_index < len(self.cpu_units) + len(self.tpu_units):
        result = self.tpu_units[best_unit_index - len(self.cpu_units)].process(data, formula_name)
    elif best_unit_index < len(self.cpu_units) + len(self.tpu_units) + len(self.gpu_units):
        result = self.gpu_units[best_unit_index - len(self.cpu_units) - len(self.tpu_units)].process(data, formula_name)
    elif best_unit_index < len(self.cpu_units) + len(self.tpu_units) + len(self.gpu_units) + len(self.lpu_units):
        result = self.lpu_units[best_unit_index - len(self.cpu_units) - len(self.tpu_units) - len(self.gpu_units)].process(data, formula_name)
    elif best_unit_index < len(self.cpu_units) + len(self.tpu_units) + len(self.gpu_units) + len(self.lpu_units) + len(self.fpga_units):
        result = self.fpga_units[best_unit_index - len(self.cpu_units) - len(self.tpu_units) - len(self.gpu_units) - len(self.lpu_units)].execute("default", data, formula_name)
    elif best_unit_index < len(self.cpu_units) + len(self.tpu_units) + len(self.gpu_units) + len(self.lpu_units) + len(self.fpga_units) + len(self.neuromorphic_units):
        result = self.neuromorphic_units[best_unit_index - len(self.cpu_units) - len(self.tpu_units) - len(self.gpu_units) - len(self.lpu_units) - len(self.fpga_units)].process(data, formula_name)
    else:
        result = self.quantum_units[best_unit_index - len(self.cpu_units) - len(self.tpu_units) - len(self.gpu_units) - len(self.lpu_units) - len(self.fpga_units) - len(self.neuromorphic_units)].process(data, formula_name)

    if api_name:
        api_call = self.api_cache.get_api_call(api_name)
        api_result = api_call()
        result = (result, api_result)

    if web_name:
        web_call = self.web_cache.get_web_call(web_name)
        web_result = web_call()
        result = (result, web_result)

    # Optimize power consumption and data communication
    self.power_manager.optimize_power(self.neuromorphic_units, [0.8, 0.5, 0.2])  # Example requirements
    transfer_time = self.communication.optimize_transfer(data_size=len(data), processors=self.neuromorphic_units)

    return result, transfer_time

Example usage

if name == "main": control_unit = ControlUnit()

# Add various processing units to control_unit
math_cache = MathCache()
control_unit.add_cpu(ModularCPU(0, math_cache))
control_unit.add_tpu(ModularTPU(0, math_cache))
control_unit.add_gpu(ModularGPU(0, math_cache))
control_unit.add_lpu(ModularLPU(0, math_cache))
control_unit.add_fpga(ModularFPGA(0, math_cache))
for i in range(10):
    control_unit.add_neuromorphic(NeuromorphicProcessor(i, math_cache))
control_unit.add_quantum(QuantumProcessor(0, math_cache))

# Add API and web integrations
control_unit.api_cache.add_api_call("example_api", lambda: "API response")
control_unit.web_cache.add_web_call("example_web", lambda: "Website response")

# Example data to process
data = np.array([1, 2, 3, 4, 5])
formula_name = "tensor_product"

# Distribute tasks to processing units with different configurations
result, transfer_time = control_unit.distribute_tasks(data, formula_name)
print(f"Result: {result}, Transfer Time: {transfer_time}")

# Fetch and process web data
fetcher = WebDataFetcher("https://api.example.com/data")
web_data = fetcher.fetch_data()

processor = DataProcessor(control_unit)
processed_results = processor.process_web_data(web_data)

for result in processed_results:
    print(result)

Complexity stages functions

def unknown_forces(data): return data * np.random.random()

def fundamental_building_blocks(data): return data + np.random.random()

def energy_infusion(data): return data * np.random.random()

def creation_of_time(data): return data + np.random.random()

def initial_breakdown_adaptation(data): return data * np.random.random()

def formation_feedback_loops(data): return data + np.random.random()

def higher_levels_feedback_memory(data): return data * np.random.random()

def adaptive_intelligence(data): return data + np.random.random()

def initial_cooperation(data): return data + np.random.random()

def adaptive_competition(data): return data * np.random.random()

def introduction_hierarchy_scale(data): return data + np.random.random()

def strategic_intelligence(data): return data * np.random.random()

def collaborative_adaptation(data): return data + np.random.random()

def competition_cooperation_supernodes(data): return data * np.random.random()

def population_dynamics(data): return data + np.random.random()

def strategic_cooperation(data): return data + np.random.random()

def modularity(data): return data * np.random.random()

def hybrid_cooperation(data): return data + np.random.random()

def strategic_competition(data): return data * np.random.random()

def hybridization(data): return data + np.random.random()

def networked_cooperation(data): return data + np.random.random()

def new_system_synthesis(data): return data * np.random.random()

def system_multiplication_population_dynamics(data): return data + np.random.random()

def interconnected_large_scale_networks(data): return data + np.random.random()

def networked_intelligence(data): return data * np.random.random()

def advanced_collaborative_partnerships(data): return data + np.random.random()

Mapping stages to functions

complexity_functions = [ unknown_forces, fundamental_building_blocks, energy_infusion, creation_of_time, initial_breakdown_adaptation, formation_feedback_loops, higher_levels_feedback_memory, adaptive_intelligence, initial_cooperation, adaptive_competition, introduction_hierarchy_scale, strategic_intelligence, collaborative_adaptation, competition_cooperation_supernodes, population_dynamics, strategic_cooperation, modularity, hybrid_cooperation, strategic_competition, hybridization, networked_cooperation, new_system_synthesis, system_multiplication_population_dynamics, interconnected_large_scale_networks, networked_intelligence, advanced_collaborative_partnerships ]

Integrating complexity stages into the task distribution

def integrate_complexity_stages(data): for func in complexity_functions: data = func(data) return data

Distribute tasks with integrated complexity stages

def distribute_tasks_with_complexity(control_unit, data, formula_name=None, api_name=None, web_name=None): data = integrate_complexity_stages(data) return control_unit.distribute_tasks(data, formula_name, api_name, web_name)

Example usage with complexity stages

new_data = np.random.rand(10) result, transfer_time = distribute_tasks_with_complexity(control_unit, new_data, formula_name) print(f"Result: {result}, Transfer Time: {transfer_time}")