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 class Program
    {
        /// <summary>
        /// This problem is an optimization problem specifically 0-1 Knapsack problem which can be solved using dynamic programming approach 
        /// </summary>
        /// <param name="args"></param>
        static void Main(string[] args)
        {
            IList<Tuple<int, int, double>> transactions = new List<Tuple<int, int, double>>();

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The introduction to Reactive Programming you've been missing

(by @andrestaltz)

---

This tutorial as a series of videos

If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.

---

Habush

__author__ = 'Abdulrahman Semrie<[email protected]>'

import os
from opencog.scheme_wrapper import scheme_eval, scheme_eval_h
from opencog.atomspace import AtomSpace
from multiprocessing import Pool

PROJECT_ROOT = os.path.dirname(os.path.abspath(__file__))

OPENCOG_DEPS_PATH = os.path.join(PROJECT_ROOT, "opencog_deps")

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(use-modules (srfi srfi-1))

;;Returns a namespace EvaluationLink of namespaces
(define namespace-eval (lambda (nsp)
    (map (lambda (n)
          (EvaluationLink
               (PredicateNode "GO_namespace")
                 (ListLink
                   (VariableNode "$a")
                  (ConceptNode n)

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(use-modules (rnrs bytevectors))
(import (uuid))
(import (industria base64))

(define generate-id (lambda ()
    (let* (
        [vecs (bytevector->u8-list (random-uuid))]
        [ls (let loop (
            (i 0)
            (ls '())

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(define (atomese->graph in out)
    (define (expr->graph thing)
        (match (cog-type thing)
                ;; nodes
                ((or 'PredicateNode
                        'GeneNode
                        'MoleculeNode 'ConceptNode)  (format #f "~s~%" (cog-name thing)))


                ;; links

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def plot_emb_projection(X, y=None, ker=tanimoto_v2, alpha=0.5, params=None, annotate=False):
    """
    Plot the row vectors of X and features of X in the same embedded space spanned by PCA Components
    :param X: the data matrix or dataframe
    :param y: the target variable (for labelling)
    :param ker: the kernel function to use
    :param alpha: the exponent to use for matrix factorization
    :return: The pca projects of the row vectors and the columns
    """