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README.md

DUMBASS Coin

DUMBASS is short for Decentralized Universal Multifunction Bazinga Artificial Stupid System.

We are the world First Greatest Purest AI + Blockchain.

How DUMBASS works

Our chain is hard forked every time from genesis block. All things happened in genesis, this made our chain the world fastest safest chain.

All Code goes here

from numpy import exp, array, random, dot
import hashlib


class Block():
    def __init__(self, input, output):
        genesis_block = str(input) + str(output)
        genesis_block += "ceo_money = 9999999999999999999999999999999999999999"
        self.blockHeight = 0
        self.blockDB = open("blocks", "w")
        self.blockDB.write("======================== " + str(self.blockHeight) + " ========================\n")
        self.blockDB.write(genesis_block)
        self.last_hash = hashlib.sha256(genesis_block).hexdigest()

    def new_block(self, data):
        self.blockHeight += 1
        self.blockDB.write("\n======================== " + str(self.blockHeight) + " ========================\n")
        self.blockDB.write(self.last_hash + "\n")
        self.blockDB.write(data)
        self.last_hash = hashlib.sha256(data).hexdigest()


class NeuralNetwork():
    def __init__(self, block):
        self.block = block
        # Seed the random number generator, so it generates the same numbers
        # every time the program runs.
        random.seed(1)

        # We model a single neuron, with 3 input connections and 1 output connection.
        # We assign random weights to a 3 x 1 matrix, with values in the range -1 to 1
        # and mean 0.
        self.synaptic_weights = 2 * random.random((3, 1)) - 1

    # The Sigmoid function, which describes an S shaped curve.
    # We pass the weighted sum of the inputs through this function to
    # normalise them between 0 and 1.
    def __sigmoid(self, x):
        return 1 / (1 + exp(-x))

    # The derivative of the Sigmoid function.
    # This is the gradient of the Sigmoid curve.
    # It indicates how confident we are about the existing weight.
    def __sigmoid_derivative(self, x):
        return x * (1 - x)

    # We train the neural network through a process of trial and error.
    # Adjusting the synaptic weights each time.
    def train(self, training_set_inputs, training_set_outputs, number_of_training_iterations):
        for iteration in xrange(number_of_training_iterations):
            # Pass the training set through our neural network (a single neuron).
            output = self.think(training_set_inputs)

            # Calculate the error (The difference between the desired output
            # and the predicted output).
            error = training_set_outputs - output

            # Multiply the error by the input and again by the gradient of the Sigmoid curve.
            # This means less confident weights are adjusted more.
            # This means inputs, which are zero, do not cause changes to the weights.
            adjustment = dot(training_set_inputs.T, error * self.__sigmoid_derivative(output))

            # Adjust the weights.
            self.synaptic_weights += adjustment
            # Make Block
            self.block.new_block(str(output) + str(self.synaptic_weights))

    # The neural network thinks.
    def think(self, inputs):
        # Pass inputs through our neural network (our single neuron).
        return self.__sigmoid(dot(inputs, self.synaptic_weights))


if __name__ == "__main__":
    # The training set. We have 4 examples, each consisting of 3 input values
    # and 1 output value.
    training_set_inputs = array([[0, 0, 1], [1, 1, 1], [1, 0, 1], [0, 1, 1]])
    training_set_outputs = array([[0, 1, 1, 0]]).T

    # Initialise blockchain
    blockchain = Block(training_set_inputs, training_set_outputs)

    # Initialise a single neuron neural network.
    neural_network = NeuralNetwork(blockchain)

    print "Random starting synaptic weights: "
    print neural_network.synaptic_weights

    # Train the neural network using a training set.
    # Do it 10,000 times and make small adjustments each time.
    neural_network.train(training_set_inputs, training_set_outputs, 10000)

    print "New synaptic weights after training: "
    print neural_network.synaptic_weights

    # Test the neural network with a new situation.
    print "Considering new situation [1, 0, 0] -> ?: "
    print neural_network.think(array([1, 0, 0]))

Team

  • CEO:
    • I'm CEO bitch!
  • COO:
    • McDonald Trump
  • Programmers:
    • Geoffrey Xinton: did the AI stuff.
    • Satoshi Nakamoto: did the blockchain stuff.
    • Guido van Rossum: for the Python stuff.

For Angels & VCs & PEs

This project values $1,000,000,000. Send the money first, then I will give you some share.

Pre sale

You can submit a stupid PR, by adding your account to genesis block. I will judge your stupid PR by your nation and skin color. The result depends on if I'm happy.

Or, pay me money directly. Of course, I may run away when it makes up 1$

paypal

Road Map

  • Tomorrow: Add IoT, Drones, and Solar Power.
  • Oct 2020: Every animal will have a DUMBASS wallet.
  • Mar 2119: Make DUMBASS Interplanetary.
  • Aprl 2190: Make cosmos peace.

How we make money

We will IPO, ICO, IBO, STO, IEO, make money from you stupid.

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