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// dependency for TabLayout
compile 'com.android.support:design:24.1.1'

//  xml
<android.support.design.widget.AppBarLayout
        android:layout_width="match_parent"
        android:layout_height="wrap_content"
        android:theme="@style/ThemeOverlay.AppCompat.Dark.ActionBar">

        <include

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// first add these to classes :
https://github.com/frogermcs/InstaMaterial/blob/master/app/src/main/java/io/github/froger/instamaterial/ui/view/FeedContextMenu.java
https://github.com/frogermcs/InstaMaterial/blob/master/app/src/main/java/io/github/froger/instamaterial/ui/view/FeedContextMenuManager.java

// Then add dependency ButterKnife :
// butterknife
compile 'com.jakewharton:butterknife:8.4.0'
annotationProcessor 'com.jakewharton:butterknife-compiler:8.4.0'

// add view_context_menu.xml the layout of the menu :

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// create new directive with cli :
ng g d directive-name

// directives are used as attribute to existant html element ex :
<div direcrive-name> ... </div>

// example of a directive :

@Directive({
  selector: '[myDirectives]'

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import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.preprocessing import Imputer
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.linear_model import LinearRegression

# Importing the dataset
dataset = pd.read_csv('train.csv')
df = pd.DataFrame(dataset)

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import numpy as np
from PIL import ImageGrab
import cv2

def draw_detections(img, rects, thickness = 1):
    for x, y, w, h in rects:
        pad_w, pad_h = int(0.15*w), int(0.05*h)
        cv2.rectangle(img, (x+pad_w, y+pad_h), (x+w-pad_w, y+h-pad_h), (0, 255, 0), thickness)

hog = cv2.HOGDescriptor()

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// styles.scss
@import '~@angular/material/prebuilt-themes/indigo-pink.css'; 


// HTML
<mat-table class="lessons-table mat-elevation-z8" [dataSource]="dataSource">

  <ng-container matColumnDef="id">
    <mat-header-cell *matHeaderCellDef>#</mat-header-cell>
    <mat-cell *matCellDef="let customer">{{customer.id}}</mat-cell>

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// Cucmumber datatable to spark dataframe
import scala.collection.convert.ImplicitConversions.`collection AsScalaIterable`
import io.cucumber.datatable.DataTable
import org.apache.spark.sql.DataFrame

def dataTableToDataframe(table: DataTable): DataFrame = {
  import sparkSession.implicits._
  val columns: Seq[String] = table.cells().head.toSeq
  val data = table.cells().drop(1).toSeq.map(r => r.toList)
  data.toDF().select(columns.indices.map(i => col("value")(i).alias(columns(i))): _*)

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// when you have a skewed data when joining this method will fix it
def saltedJoin(df: DataFrame, buildDf: DataFrame, joinExpression: Column, joinType: String, salt: Int): DataFrame = {
   import org.apache.spark.sql.functions._
   val tmpDf = buildDf.withColumn("slt_range", array(Range(0, salt).toList.map(lit): _*))

   val tableDf = tmpDf.withColumn("slt_ratio_s", explode(tmpDf("slt_range"))).drop("slt_range")
   val streamDf = df.withColumn("slt_ratio", monotonically_increasing_id % salt)

   val saltedExpr = streamDf("slt_ratio") === tableDf("slt_ratio_s") && joinExpression
   streamDf.join(tableDf, saltedExpr,     joinType).drop("slt_ratio_s").drop("slt_ratio")

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// case FetchFailedException or MetadataFetchFailedException: how to avoid BroadcastNestedLoopJoin
- spark.executor.memoryOverhead=1g
- spark.kubernetes.memoryOverheadFactor=0.2

// avoid skew spark >= 3.0
spark.sql.adaptive.optimizeSkewedJoin.enabled

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// Hadoop
Download winutils.exe and hadoop.dll: https://github.com/kontext-tech/winutils
add them inside older C:\hadoop\bin
add env varible hadoop.home.dir and HADOOP_HOME, with value = C:\hadoop
add %HADOOP_HOME%\bin to the path
add hadoop.dll to C:\Windows\system32
Make sure your JVM is 64 bit.

// Spark
download spark from https://spark.apache.org/downloads.html