alekseyl1992

%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt

xs = np.arange(-100, 100, 0.1)
ys = np.sin(xs)

n = 75

def mcloren(x):

alekseyl1992

def get_area(matrix, start_row, start_col):
    max_area = 0
    height = 0
    prev_line_width = 0

    for j in range(start_row, len(matrix)):
        row = matrix[start_row]
        height += 1
        current_line_width = 0

alekseyl1992

#include <iostream>
#include <memory>
#include <string>
#include <vector>

template <typename T>
class my_shared_ptr {
public:
    my_shared_ptr(T* raw_ptr)
        : raw_ptr_(raw_ptr),

alekseyl1992

#include <iostream>
#include <memory>
#include <string>
#include <vector>

template <typename T>
class my_shared_ptr {
public:
    my_shared_ptr(T* raw_ptr)
        : raw_ptr_(raw_ptr),

alekseyl1992

    Index             url     score  score_delta                       uid  time_delta          speed                      date
0   52790    /start_game:         0            0  57519da4fa2950be58e0645c       0.000       0.000000  2016-06-03T15:11:11.049Z
1   52258  /update_score:         0            0  57519da4fa2950be58e0645c     125.641       0.000000  2016-06-03T15:11:30.408Z
2   52266    /start_game:         0            0  57519da4fa2950be58e0645c       0.000       0.000000  2016-06-03T15:11:53.763Z
3   52799  /update_score:         1            1  57519da4fa2950be58e0645c      33.096       0.030215  2016-06-03T15:12:03.504Z
4   52801  /update_score:         1            0  57519da4fa2950be58e0645c       1.593       0.000000  2016-06-03T15:12:05.097Z
5   52804  /update_score:         2            1  57519da4fa2950be58e0645c      18.421       0.054286  2016-06-03T15:12:23.518Z
6   52276  /update_score:         3            1  57519da4fa2950be58e0645c       9.985       0.100150  2016-06-03T15:12:33.503Z

alekseyl1992

from __future__ import print_function

from pyspark import SparkContext, SparkConf
from pyspark.mllib.linalg import DenseVector, VectorUDT
from pyspark.sql import SQLContext

from pyspark.ml.classification import MultilayerPerceptronClassifier
from pyspark.ml.evaluation import MulticlassClassificationEvaluator
from pyspark.sql.types import StructType, StructField, StringType, DoubleType, ArrayType

alekseyl1992

import random

def sample(p):
    x, y = random.random(), random.random()
    return 1 if x*x + y*y < 1 else 0

def run(sc, num_samples):
    count = sc.parallelize(xrange(0, num_samples)).map(sample) \
                 .reduce(lambda a, b: a + b)
    print "Pi is roughly %f" % (4.0 * count / num_samples)

alekseyl1992

#include <exception>
#include <stdexcept>
#include <string>

class NotImplementedException : public std::runtime_error {
public:
    NotImplementedException(const std::string& str)
        : std::runtime_error(str) {

    }

alekseyl1992

package main

import (
	"fmt"
	"net"
	"os"
)

const (
	CONN_HOST = "localhost"

alekseyl1992

using Generation = std::vector<Genome>;

constexpr int buildingSize = 100;

Genome createGenome() {
    Genome genome;
    int currentFloor = 0;

    do {