View README.md

Install

# kubernetes
brew cask install minikube
minikube start

# helm
brew install kubernetes-helm
helm init
View main.go
package main
import (
"fmt"
"io"
"log"
"net"
"os"
"github.com/djherbis/bufit"
View clojush.proto
message Run {
required Configuration config = 1;
repeated Generation generations = 2;
}
message Configuration {
required string problemFile = 1;
// arguments are stored as EDN export of clojure types
required map<string, string> arguments = 2;
View ideal point.ipynb
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View README.md

This is a working example on how to store CryptoKeys locally in your browser. We are able to save the objects, without serializing them. This means we can keep them not exportable (which might be more secure?? not sure what attack vectors this prevents).

To try out this example, first make sure you are in a browser that has support for async...await and indexedDB (latest chrome canary with chrome://flags "Enable Experimental Javascript" works). Load some page and copy and paste this code into the console. Then call encryptDataSaveKey(). This will create a private/public key pair and encrypted some random data with the private key. Then save both of them. Now reload the page, copy in the code, and run loadKeyDecryptData(). It will load the keys and encrypted data and decrypt it. You should see the same data logged both times.

View test.clj
(require '[search.core])
(require 'plumbing.fnk.schema)
(require 'plumbing.fnk.pfnk)
(require '[plumbing.graph :as g])
(require 'rhizome.viz)
(require '[search.utils :as utils])
(def g_ (:final-graph (g/run search.core/config->run-graph (search.core/->config {:graph-symbols '(search.graphs.problems.push-sr/plus-six-graph search.graphs.algorithms.genetic/graph)}))))
View script.sh
$ emacs -Q --batch --eval '(add-to-list \'load-path "~/installs/slime")' --eval '(setq inferior-lisp-program (executable-find "sbcl"))' --eval '(require \'slime-autoloads)' --eval '(require \'slime)' --eval '(slime-setup)' --eval '(slime)' --eval '(while (not (slime-connected-p)) (sit-for 1))' --eval '(require \'json)' --eval '(princ (json-encode (second (slime-eval `(swank:compile-file-for-emacs "src/graph.lisp" t)))))' | jq
Polling "/var/folders/xl/z_j_yt_s5z18y3zdqg24tn180000gn/T/slime.20749" .. 1 (Abort with `M-x slime-abort-connection'.)
Polling "/var/folders/xl/z_j_yt_s5z18y3zdqg24tn180000gn/T/slime.20749" .. 2 (Abort with `M-x slime-abort-connection'.)
Connecting to Swank on port 58411..
Connected. Hack and be merry!
{
"message": [
"undefined variable: NODE",
"severity",
"warning",
View 202.elm
{-|
> ```
Consider a simple model for whether a person has the flu or not. Let F=1
indicate that a person has the flu and F=0 indicate that they don't have the
flu. Let C=1 indicate that the person has a cough and C=0 indicate that they
don't have a cough. Let M=1 indicate that the person has muscle pain and M=0
indicate that they don't have muscle pain. Assume that C and M are conditionally
independent given F so that the probability model is
P(C=c,M=m,F=f)=P(C=c|F=f)P(M=m|F=f)P(F=f).
Suppose that we ask two different doctors to supply probabilities for this model
View test.md

Consider a simple model for whether a person has the flu or not. Let F=1 indicate that a person has the flu and F=0 indicate that they don't have the flu. Let C=1 indicate that the person has a cough and C=0 indicate that they don't have a cough. Let M=1 indicate that the person has muscle pain and M=0 indicate that they don't have muscle pain. Assume that C and M are conditionally independent given F so that the probability model is P(C=c,M=m,F=f)=P(C=c|F=f)P(M=m|F=f)P(F=f). Suppose that we ask two different doctors to supply probabilities for this model and we obtain the following results:

View .md

Question

Consider a simple model for whether a person has the flu or not. Let F=1 indicate that a person has the flu and F=0 indicate that they don't have the flu. Let C=1 indicate that the person has a cough and C=0 indicate that they don't have a cough. Let M=1 indicate that the person has muscle pain and M=0 indicate that they don't have muscle pain. Assume that C and M are conditionally independent given F so that the probability model is P(C=c,M=m,F=f)=P(C=c|F=f)P(M=m|F=f)P(F=f).

Suppose that we ask two different doctors to supply probabilities for this model and we obtain the following results: Doctor 1: P(F=1)=0.4 P(C=1|F=0)=0.2, P(C=1|F=1)=0.8 P(M=1|F=0)=0.3, P(M=1|F=1)=0.9