ramannanda9

class MetricsProvider(abc.ABC):
  @abc.abstractmethod
  def name():
    pass
  @abc.abstractmethod
  def fetch_metrics(tags:Dict[str,str], strategy: Strategy)->Dict[str, Metric]:
    #should fetch metrics, combination happens on server side as per strategy i.e. rolling average to reduce load in data transfer
    pass
  @abc.abstractmethod
  def append_metrics(tags: Dict[str, str], values: Union[Any, pd.Series]) -> None:

ramannanda9

import atexit
import copy
import dataclasses
import logging
import os

from main import User, UserManager

logging.basicConfig(level=logging.INFO)
# use the below logger

ramannanda9

# This is a sample Python script.

# Press ⌃R to execute it or replace it with your code.
# Press Double ⇧ to search everywhere for classes, files, tool windows, actions, and settings.
import dataclasses
import json
import logging
from json import JSONEncoder
from typing import Dict, List

ramannanda9

package com.orastack;

import java.lang.reflect.Array;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.Collections;
import java.util.List;
import java.util.stream.Stream;
import lombok.AllArgsConstructor;
import lombok.Getter;

ramannanda9

class StonesProblem {
   int[][] stageScore=null;
    public boolean stoneGame(int[] piles) {
      stageScore= new int[piles.length+2][piles.length+2];
      // stoneGameRec(0, piles, true,0,piles.length-1)>0
      return stoneGameBottomUp(piles)>0;
    }
  
    public int stoneGameRec(int sumSoFar, int [] piles, boolean positive, int begin, int end){
      if(stageScore[begin][end]!=0){

ramannanda9

/*package whatever //do not write package name here */

import java.util.*;
import java.lang.*;
import java.io.*;
import java.util.Scanner;

class GFG {
    
    public int getMinDistance(String[] words,String word1,String word2){

ramannanda9

val conf = new NeuralNetConfiguration.Builder().regularization(true).l2(0.001).weightInit(WeightInit.RELU)
  .updater(new Adam(0.01, 0.9, .98, 1e-8)).
  optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).
  convolutionMode(ConvolutionMode.Strict).list().
  layer(0, new ConvolutionLayer.Builder(7, 7).nIn(1).stride(1, 1)
    .nOut(25).activation(Activation.IDENTITY).build())
  .layer(1, new SubsamplingLayer.Builder(PoolingType.MAX).
    kernelSize(2, 2).stride(2, 2).build()).
  layer(2, new ConvolutionLayer.Builder(6, 6).stride(1, 1).nOut(64).
    activation(Activation.IDENTITY).build()).

ramannanda9

def filter[K](list: List[K], f: K => Boolean): List[K] = {
  @tailrec
  def filterRec(list: List[K], f: K => Boolean): List[K] = list match {
    case Nil => list
    case list: List[K] => if (f(list.head)) filterRec(list.head :: list.tail, f) else filterRec(list.tail, f)
  }
  filterRec(list, f)
}

val listToFilter = 2 :: 5 :: Nil

ramannanda9

public class MaxSumInTree{
int left(int i) {
    return (2 * i + 1);
  }

  int right(int i) {
    return (2 * i + 2);
  }

  long findMax(int n, String tree) {

ramannanda9

def filter[K](arr: List[K], f: K => Boolean): List[K] = arr match {
  case Nil => arr
  case list: List[K] => if (f(list.head)) list.head :: filter(list.tail, f) else filter(list.tail, f)
}

val listToFilter = 2 :: 5 :: Nil

listToFilter.filter(_ > 2)