forked from cheng/wallet
finally figured out how to social network lookups
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@ -90,7 +90,7 @@ The optimal instance recognition algorithm, for normally distributed
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attributes, and for already existent, already known categories, is Mahalanobis
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distance
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Is not the spam characteristic of an email just its $T.(S-G)$, where $T$ is
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Is not the spam probability of an email just its $T.(S-G)$, where $T$ is
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the vector of the email, and $S$ and $G$ are the average vectors of good
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email and spam email?
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@ -103,9 +103,18 @@ normal.
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But variance is, approximately, the log of probability, so Mahalanobis is
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more or less Bayes filtering, or at least one can be derived in terms of the other.
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So we can reasonably reduce each email into twenty questions space, or, just
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to be on the safe side, forty questions space. (Will have to test how many
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dimensions empirically retain angles and distances)
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So we can reasonably reduce each email into twenty questions space, albeit in practice, a great deal more than twenty. Finding far from random
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dimensions that reduce it to a mere twenty or so is an artificial intelligence
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hard problem. If random dimensions, need $\bigcirc20\log{(n)}$ dimensions
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where $n$ is the number of things. And $n$ is apt to be very large.
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Finding interesting and relevant dimensions, and ignoring irrelevant and
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uninteresting dimensions, is the big problem. It is the tie between
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categorizing the world into natural kinds and seeing what matters in the
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perceptual data while ignoring what is trivial and irrelevant. This requires
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non trivial non local and non linear combinations of data, for example
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adjusting the perceived colour of the apple for shadow and light colour, so
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see the ample, rather than merely the light scattered by the apple into the eye.
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We then, in the reduced space, find natural groupings, a natural grouping
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being an elliptic region in high dimensional space where the density is
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@ -363,6 +363,34 @@ way hash, so are not easily linked to who is posting in the feed.
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This handles public posts.
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### Kademlia in social space
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The vector of each identity is a sequence ones and zeros of unbounded
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length, unboundedly large dimension, but in practice you will not need
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anything beyond the first few hundred.
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We deterministically generate the vector by hashing the public key of the identity.
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We do not have, and do not need, an exact consensus on how much of the
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vector to actually use, but everyone needs to use roughly the same amount
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as everyone else. The amount is adjusted according to what is, over time,
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needed, by each identity adjusting according to circumstances, with the
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result that over time the consensus adjusts to what is needed.
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Each party indicates what entities he can provide a direct link to by
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publishing the sum of the vectors of the parties he can link to - and also
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the sum of the their sums, and also the sum of their ... to as many deep as
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turns out to be needed in practice, which is likely to two or three such
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vector sums, maybe four or five.
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When someone is looking to find how to connect to an identity, he goes
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through the entities he can connect to, and looks at the dot product of
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their sum vectors with target vector.
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He contacts the closest entity, or a close entity, and if that does not work
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out, contacts another. The closest entity will likely be able to contact
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the target, or contact an entity more likely to contact the target.
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## Private messaging
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Private messaging is trivial. There is no end of excellent existing software
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@ -307,13 +307,15 @@ In this table, edited in a fixed font, you are using whitespace and blank lines
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### Grid tables
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Allows multiline, and alignment, but visual studio does not like it, and you still have to count those spacees
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Allows multiline, and alignment, but visual studio does not like it, and you still have to count those spaces
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+---------------+---------------+--------------------+
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| Fruit | Price | Advantages |
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+===============+==============:+====================+
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| Bananas | $1.34 | - built-in wrapper |
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| | | - bright color |
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| Bananas | $1.34 | Mary had a little lamb whose fleece was white as snow, and everywhere that |
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| | | Mary went the lamb was sure to go |
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| | | |
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| | | bright color |
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+---------------+---------------+--------------------+
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| Oranges | $2.10 | - cures scurvy |
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| | | - tasty |
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@ -479,7 +481,16 @@ defined by very small source code.
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font-weight="400"
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stroke-width="2"
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style="text-decoration:underline; cursor:pointer;" >
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<line x1="22" y1="70" x2="28" y2="100" stroke="lightgrey"/>
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<path fill="none" stroke="#800000"
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d="M45 100, c100 -50, -40 -50, 60 0" />
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<line x1="22" y1="70" x2="28" y2="100" stroke="grey"/>
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<rect x="60" y="64" width="20" height="20">
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<animate attributeType="XML" attributeName="y"
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from="64" to="120"
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dur="3s" repeatCount="5" restart="whenNotActive"/>
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<animate attributeType="XML" attributeName="x"
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from="60" to="0"
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dur="3s" repeatCount="5" restart="whenNotActive"/></rect>
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<rect style="fill:#FFFF00;"
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x="12" y="64" width="36" height="20">
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<animate attributeType="XML" attributeName="y"
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@ -493,19 +504,12 @@ defined by very small source code.
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start animation
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</text>
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</g>
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<rect x="60" y="64" width="20" height="20">
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<animate attributeType="XML" attributeName="y"
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from="64" to="120"
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dur="3s" repeatCount="5" restart="whenNotActive"/>
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<animate attributeType="XML" attributeName="x"
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from="60" to="0"
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dur="3s" repeatCount="5" restart="whenNotActive"/></rect>
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<g
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font-family="'Times New Roman'" font-size="5"
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font-weight="400"
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stroke-width="2">
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<path fill="none" stroke="#00f000"
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d="M14 101, c40 -20, 30 -56, 54 -18, s60 15, 40 15 c -20,0 -10,-20 0,-20 q 5,0 10,10" />
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d="M14 101, c40 -20, 30 -56, 54 -18 s60 15, 40 15 c -20,0 -10,-20 0,-20 q 5,0 10,10" />
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<ellipse cx="60" cy="85" rx="12" ry="5" style="fill:red" />
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<text x="60" y="82" text-anchor="middle" style="fill:#A050C0;" >
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A simple scalable vector graphic
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