2024-07-02

"""
Dave Husk

I'm starting to upload all my stuff.... god help me trying to organize it all...

"""
import math
import sympy as sp
from fractions import Fraction

# 1. Fibonacci-Inspired Salmon Equation Solver
def salmon_equation_solver(a, b, c):
    delta = b**2 - 4*a*c
    if delta == 5:
        root1 = (-b + math.sqrt(delta)) / (2*a)
        root2 = (-b - math.sqrt(delta)) / (2*a)
        return (root1, root2)
    else:
        return "Error: Discriminant not equal to 5"

# 2. Continued Fraction Representation of h
def continued_fraction_h(n):
    h_cf = Fraction(1, 1)
    for _ in range(n-1):
        h_cf = 1 + 1 / h_cf
    return h_cf

# 3. Galois Group Symmetry
def galois_group_symmetry(a, b, c):
    x = sp.symbols('x')
    poly = sp.Poly(a*x**3 - 3*b*x**2 + c*x - 1, x)
    galois_group = poly.galois_group()
    return galois_group

# 4. Diophantine Equation Solver
def diophantine_solver(a, b, c):
    x, y = sp.symbols('x y')
    diophantine_eq = sp.Eq(a*x + b*y, c)
    solutions = sp.diophantine(diophantine_eq)
    return solutions

# Define the variables for testing
a, b, c = 1, -3, 1

# Test the Salmon equation solver
roots = salmon_equation_solver(a, b, c)

# Test the continued fraction representation
n = 5
h_cf = continued_fraction_h(n)

# Test the Galois group symmetry
galois_group = galois_group_symmetry(a, b, c)

# Test the Diophantine equation solver
diophantine_solutions = diophantine_solver(3, 5, 7)

roots, h_cf, galois_group, diophantine_solutions

"""((2.618033988749895, 0.3819660112501051), Fraction(8, 5), (PermutationGroup([
    (0 1 2),
    (2)(0 1)]), False), {(5*t_0 + 14, -3*t_0 - 7)})"""


import numpy as np

class ThoughtTree:
    def __init__(self, token, response_matrix):
        self.token = token
        self.response_matrix = response_matrix
        self.children = []

    def add_child(self, child):
        self.children.append(child)

    def reflect(self, freq_matrix, input_token):
        # Calculate the Hurwitz Zeta frequency of the current node
        zeta_freq = np.dot(freq_matrix, self.token)

        # Generate a response based on the input token and the Hurwitz Zeta frequency
        response = np.dot(self.response_matrix, input_token)
        response = np.argmax(response)

        # Reflect on the children nodes based on the Hurwitz Zeta frequency
        for child in self.children:
            child.reflect(freq_matrix, response)

    def respond(self, input_token):
        # Calculate the response based on the input token and the response matrix
        response = np.dot(self.response_matrix, input_token)
        response = np.argmax(response)

        # Return the response
        return response

    def execute(self, indent=0):
        print('  ' * indent + str(self.token))
        for child in self.children:
            child.execute(indent + 1)

# Create the frequency matrix
freq_matrix = np.random.rand(10, 10)

# Create the response matrix
response_matrix = np.random.rand(10, 10)

# Create the thought tree
root = ThoughtTree(np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]), response_matrix)
child1 = ThoughtTree(np.array([11, 12, 13, 14, 15, 16, 17, 18, 19, 20]), response_matrix)
child2 = ThoughtTree(np.array([21, 22, 23, 24, 25, 26, 27, 28, 29, 30]), response_matrix)
child3 = ThoughtTree(np.array([31, 32, 33, 34, 35, 36, 37, 38, 39, 40]), response_matrix)

root.add_child(child1)
root.add_child(child2)
child1.add_child(child3)

# Test the thought tree
input_token = np.array([41, 42, 43, 44, 45, 46, 47, 48, 49, 50])
response = root.respond(input_token)
response_result = response

# Reflect on the thought tree
root.reflect(freq_matrix, input_token)

# Execute the thought tree
execute_result = root.execute()

response_result, execute_result

"""
[ 1  2  3  4  5  6  7  8  9 10]
  [11 12 13 14 15 16 17 18 19 20]
    [31 32 33 34 35 36 37 38 39 40]
  [21 22 23 24 25 26 27 28 29 30]
(2, None)
"""


import numpy as np
import random

class ThoughtTree:
    def __init__(self, token, response_matrix):
        self.token = token
        self.response_matrix = response_matrix
        self.children = []

    def add_child(self, child):
        self.children.append(child)

    def reflect(self, freq_matrix, input_token):
        # Calculate the Hurwitz Zeta frequency of the current node
        zeta_freq = np.dot(freq_matrix, self.token)

        # Generate a response based on the input token and the Hurwitz Zeta frequency
        response = np.dot(self.response_matrix, input_token)
        response = np.argmax(response)

        # Reflect on the children nodes based on the Hurwitz Zeta frequency
        for child in self.children:
            child.reflect(freq_matrix, response)

    def respond(self, input_token):
        # Calculate the response based on the input token and the response matrix
        response = np.dot(self.response_matrix, input_token)
        response = np.argmax(response)

        # Return the response
        return response

    def execute(self, indent=0):
        print('  ' * indent + str(self.token))
        for child in self.children:
            child.execute(indent + 1)

# Create the frequency matrix
freq_matrix = np.random.rand(10, 10)

# Create the response matrix
response_matrix = np.random.rand(10, 10)

# Create the thought tree
root = ThoughtTree(np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]), response_matrix)
child1 = ThoughtTree(np.array([11, 12, 13, 14, 15, 16, 17, 18, 19, 20]), response_matrix)
child2 = ThoughtTree(np.array([21, 22, 23, 24, 25, 26, 27, 28, 29, 30]), response_matrix)
child3 = ThoughtTree(np.array([31, 32, 33, 34, 35, 36, 37, 38, 39, 40]), response_matrix)

root.add_child(child1)
root.add_child(child2)
child1.add_child(child3)

# Test the thought tree
input_token = np.array([41, 42, 43, 44, 45, 46, 47, 48, 49, 50])
response = root.respond(input_token)
response, root.execute()

# Reflect on the thought tree
root.reflect(freq_matrix, input_token)

"""
[ 1  2  3  4  5  6  7  8  9 10]
  [11 12 13 14 15 16 17 18 19 20]
    [31 32 33 34 35 36 37 38 39 40]
  [21 22 23 24 25 26 27 28 29 30]
(6, None)
"""