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Dodeca

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About Dodeca

  • Rank
    Member

Personality

  • MBTI
    xxxx
  • Astrology Sign
    Scorpio
  • Personal DNA
    Considerate Thinker
  • Brain Dominance
    4

Converted

  • Location
    New Mexico
  • Interests
    Internet
  • Gender
    Male
  • Personal Text
    Yes we have a soul. But its made of lots of tiny robots. -Giulio Giorelli
  1. The idea I had was what if associations could form associations. But what you said made me think about exclusion. It is the case that people in a group can make or break associations at will. It then becomes a matter of which people associate with whom and with when. Groups form such that a member is excluded from other groups. So even though 3 people can be in 7 possible groups only 4 can happen because of exclusion. If there are 3 people in a network they can form 7 possible group associations. set1 = {1, 2, 3} one group of 3 set2 = {{1}{2}{3}} 3 isolated people set3 = {{1, 2}{2, 3}{1, 3}} 3 groups of two people each But with exclusion a = {1, 2, 3} b = {{1}{2}{3}} c = {{1, 2}{3}} d = {{2, 3}{1}} a sequence can then be formed (s) is (a.b.c.d) or S = 4. This is s^s combinations or 4^4 = 256 so a group of 3 people can form 4 kinds of groups and with 4 transitions (a.b.c.d) 256 possible group sequences {1, 2, 3} becomes {{1}{2}{3}} becomes {{1, 2}{3}} becomes {{2, 3}{1}} 1 group then group 2 then group 3 then group 4 sequence (a.a.a.a) is another possibility. If there are 7 people 43 possible groups but because of s^s i do not know how to find s? I think more letters of the alphabet are needed with 7 people?
  2. When I first thought of it it did reach into higher dimension space. When two or more nodes form a connection as groups such as in a tetrahedron with 5 points (the fifth being in the center connecting all four) if you add more nodes / points then the minimum shape / structure needs to add a dimension such that 4 point can be in a triangle but 5 can not. 5 need to be a tetrahedron and then triangles form on the sides. Each group of nodes / points (two plus) form a new point / node. So the sequence (1, 2, 3, 7, 43) are the creation of new group formations that have a hyperdimension shape with groups being (2 to n). This way like triangles embed in tetrahedrons the groups that form are the minimum hyperdimensional shape to contain all groups without cris cross. The number of new groups formed is the relationship between all the ways (n) can be grouped. If you take 7 people there are 43 ways to form groups. This has a minimum hyperdimensional shape. A group of 7 people can become 43 different kinds of networks. but only if you want the minimalist shape does it matter (triangle / tetrahedron) groups still can form without higher dimensions. ---------- Post added 06-03-2015 at 12:59 PM ---------- Also to mention: It would be that the smallest hyper volume is possible by group formations. Group interactions can be mapped this way I think?
  3. I got confused by the first two numbers in the sequence (1, 2). I adjusted it so it works with networks starting with 3 nodes: Fn = ((F(n-1)-1)*F(n-1))+1 3 = ((1)*2)+1 7 = ((2)*3)+1 43 = ((6)*7)+1 1,807 = ((42)*43)+1 3,263,443 = ((1806)*1807)+1 10,650,056,950,807 = ((3,263,442)*3,263,443)+1 actually this adds to 43: 7 + 7 + 7 + 7 +7 + 7 + 1 = 43 the first two recursions really messed up my thinking
  4. I have devised a formula for generating new nodes in a network where as a link becomes a new node and nodes form links as a set of collected nodes. 2 nodes have one link so 2 becomes 3 sets. 3 nodes can create 7 sets: 1 set of 3 3 sets of 2 3 sets of 1 and 7 nodes can create 50 sets: 7 sets of 1 7 sets of 2 7 sets of 3 7 sets of 4 7 sets of 5 7 sets of 6 1 set of 7 Here is an image of 3 nodes becoming 7 nodes: this is the formula: Fn = F(n-1)+((F(n-1)-1)*F(n-1))+1 1 = 0+((0)*0)+1 2 = 1+((0)*1)+1 3 = 2+((0)*2)+1 7 = 3+((2)*3)+1 50 = 7+((6)*7)+1 2,501 = 50+((49)*50)+1 6,255,002 = 2501+((2500)*2501)+1 39,125,050,020,005 = 6255002+((6255001)*6255002)+1 I do not know what this is but i thought i would share it. I think it might be important to some portion of network theory? Edit: I noticed it only works after the network is larger than 3.
  5. I do everything in compiled BASIC these days, since I'm just prototyping.

  6. java is much faster than python, i once made some jar files you can click on and open. i hear that C and C++ are the fastest optimized code because of the chipset they work with(intel, amd). what kind of environment do you make for ai programs?

    Simple Python Questions

  7. thanks clojure for showing me how that display works. when i ran it it froze up on my computer though, python must not run that fast. here is my entire ai program: import random def j(): return random.randrange(0, 10000, 1) def p(): return random.randrange(0, 255, 1) node_state = [] def set_node_state(): node_state = [] for i in range(1, 10000): node_state[i] = 0 input_x = [] output_x = [] def set_input_x(input_x): for i in range(1, 100): input_x[i] = (1 / 256) * p() return input_x input_node_set = [] output_node_set = [] def input_output(): for i in range(0, 100): input_node_set[i] = j() output_node_set[i] = j() cset = [] def set_csets(cset): for i in range(0, 100): cset[i] = j() return cset connection_set1 = [] connection_set2 = [] def set_connection_sets(): for i in range(0, 10000): cset = [] connection_set1.append(set_csets(cset)) cset = [] connection_set2.append(set_csets(cset)) def thalamus(): modulation = 0 for i in range(0, 100000): modulation += node_state[i] modulation = modulation / 10000 for i in range(0, 100): input_x = [] node_state[input_node_set[i]] = (set_input_x(input_x[i]) + modulation) / 2 output_x[i] = (node_state[output_node_set[i]] + modulation) / 2 node_state[output_node_set[i]] = (node_state[output_node_set[i]] + modulation) / 2 def cortex(): sum1 = [] sum2 = [] connection_values1 = [] connection_values2 = [] new_node_state1 = [] new_node_state2 = [] for i in range(1, 10000): for q in range(1, 100): connection_values1[i] += node_state[connection_set1[i:[q]]] for i in range(1, 10000): for q in range(1, 100): connection_values2[i] += node_state[connection_set2[i:[q]]] for i in range(0, 10000): sum1[i] = connection_values1[i] / 100 sum2[i] = connection_values2[i] / 100 new_node_state1[i] = sum1[i] / sum2[i] new_node_state2[i] = sum2[i] / sum1[i] for i in range(0, 100): if (connection_values1 / new_node_state1) > new_node_state1: connection_set1[i] = j() for i in range(0, 100): if (connection_values2 / new_node_state2) > new_node_state2: connection_set2[i] = j() for i in range(0, 10000): node_state[i] = (new_node_state1[i] + new_node_state2[i]) / 2 i shared these pictures with monte314 but i think he said he does not know python. These pictures explain what the ai program does. first it learns a typology from the information circulating in the cortex between 1.0 and 0.0 it compares two sets of connections averaged together for the difference between them and then changes the single connections that are a ratio of difference above that percentage of the average. the thalamus then modulate the input and output by averaging all neurons and using that value by averaging it with the input and output. with the code i wrote because i explained what it does with the pictures and the learned typology i think that anyone can use and improve it if they know how to make the code more efficient. that is why i am presenting it here so that is can be used by anyone with my explanation. because it is a miniature brain it could be used in an avatar or something. monte314 said he know about creating environments for ai systems, if he learned python or read my explanation he could code it in a language he understands.
  8. i figured out how to use lists to manipulate data and have written my main ai program functions. all i need now is to display the results in a window. each node / neuron value is between 0 and 255 there are 10,000 nodes / neurons, this make a grid of 100*100 below is the display code but i am using green rectangles of different shades because i do not know how to do individual pixels. i need help with this because i do not know how it is done: from tkinter import * root = Tk() root.title("Simple GUI") #root.geometry("400x400") w = Canvas(root, width=400, height=400) w.pack() import random def j(): return random.randrange(0, 255, 1) green_rectangle_shade = [] for x in range(0, 100): for y in range(0, 100): green_rectangle_shade[x, y] = j(); def draw_data(): w.create_rectangle(0, 0, 400, 400, fill="black") for x in range(0, 200, 2): for y in range(0, 200, 2): w.create_rectangle(x, y, x+1, y+1, fill=green_rectangle_shade[x/2, y/2]) import threading def update(): threading.Timer(0.1, update).start() draw_data() update() root.mainloop()
  9. 8 * 7 * 6 * 5 * 4 * 3 * 2 * 1 = 40,320 configurations if we have all 8 but could you clarify how you see functions as separate? what does it mean to be in a mental state of a function?
  10. i am writing an ai program in python but i do not know syntax much of. here ill try to be concise but i already know the modules of my program i just need help with syntax. the first thing i need to know is how to manipulate data in arrays: nodes = array((10,10)) nodes[5][5] = 4 print(nodes) i did some arrays in java before but i don't know how in python.
  11. The phi ratio is used to calculate the entropy because the dodechedron in the network is the best shape possible for 3D compaction where rotation is the angle of the pentagram. Tetrahedron could be used in a diamond structure but its to uniform. Asymmetry is needed to create loops in the system. Icasohedron is also possible for the grid pattern but it has 12 nodes rather than 20 in the dodecahedron so stores less negentropy. The dodecahedron fits in a 3D space with 12 dodecahedron beside it. Energy input as frequency will allow the network to absorb information reducing entropy in a 3D space like a crystal hologram. It can then use energy to do computations / pattern recognition much like a perceptron but holographic. The way i have it set up in my model does not need to be a specific typology but this typology is how i started to understand it. Entropy reduction can be done with any form information takes, it is just what kind of representations you use because i am using frequency of clusters which might be inside a 3D structure like a brain. Neurons are 3D but more complex. My model is simple but contains enough structure to reduce entropy in an environment where energy exists. Organisms have constraints of a 3D environment and energy consumption. Those organism change by building structures which links and nodes channel energy and entropy is the new configuration.
  12. f = frequency t = time i might have it written wrong by phi in relation to f such that t will take f and at intervals of 1.618 sample it. delta is if a nodes links changes with phi being in the resonance of f. so if f matches phi within the t interval it is broken. phi is the threshold of the node. it makes sense to me because my math is dealing with certain assumptions about nodes links and energy. heat is entropy inducing so phi limits the heat to an equilibrium. adaptation is the nodes ability to form links accordingly phi is also used as the ratio between nodes the link has attainment of.
  13. These are my new equations for entropy in a network of dodecahedron in the grid energy input changes how the dodecahedron makes or breaks connections. when entropy is high it reconfigure it connections for an arrangement that decreases entropy
  14. Continuing: The reason it is difficult to decide what type you are is because there are pairs which are similar to each other which leads to misinterpretations of what a function is. These are N with T and F with S. An intuitive type may view itself as thinking and thinking as intuitive. Same for an S and an F type. To elaborate i must define them in order. S types perceive through the five senses but one of them is is in the body so SF types confuse 1 with the other 4. N types will believe they think because ideas they have are creative expressions of thinking but in fact thinking deals with arrangement of facts and data not generating new material. Dealing with the introvert and the extrovert one is the internal generation of objects well the other is the relationship to external objects. Both internal and external objects are inside the subject but one is projected and the other is real. Each Type has four functions Dominant, Auxiliary, Tertiary, Inferior. Each function is utilized them to some extent but the strength of I/E in the dominate determiners how much the inferior is suppressed. Extroverted Sensation sees hears smells tastes but does not feel as F does. It is vivid as the object. Introverted Sensation view of the object will imagine objects but their imagination will be about real world events. Extroverted Intuition will take objects and morph them into things that do not exist, this is where new concepts come from objects. Introverted Intuition will imagine things that never have existed and this is the inner sight of insight because the image with tell them the new realization. Extroverted Feeling will get warm fuzzys from cute objects and they are not immune to the objects pull. To be satisfied the environment must be arranged perfectly. Introverted Feeling decides how they want to feel about an object regardless of if it does not change. They want the feeling to be what it is regardless of circumstance, if they want to feel angry or sad or happy they will do so. This is in the body. Extroverted Thinking is able to know where objects are and put them in their place. It is able to follow rules it learns and sees. Introverted Thinking is the inner voice that represents concepts. It compares thoughts to each other to see if the line up. New rules appear by this process of comparing them just as what the extroverted thinking does with objects.