When it comes out the ground, natural gas will be mixed up with other liquid or vapor hydrocarbons and water vapor. A great deal of the vapor and liquid that is caught up in the natural gas will be removed by high pressure separators. Separators are designed primarily to separate liquid from gas, though three-phase separators will also isolate liquid hydrocarbons from water. Once the natural gas has passed through a separator, however, it may still be fairly wet gas, also known as rich gas. This is gas that still contains vapor, both water and a form of liquid hydrocarbon called distillate, that needs to be removed before it can be sent to the pipeline.
A dehydration unit will often be used to dry natural gas enough for it to be sold. The gas may still contain some vapor, but it will be conditioned so that its dewpoint, the temperature at which condensation forms, is lower. Some types of dehydration units may be referred to as a thermo or stack pack.
Figure 1. A diagram outlining the operation of a glycol dehydration unit.
The gas is treated with a chemical that is hygroscopic, meaning it will absorb the water vapor in the gas. The dry gas is then measured and stored or sold. That chemical is usually either tri-ethylene glycol or ethylene glycol, with tri-ethylene glycol being more common. The dehydration unit will usually be with the separator, located together before the tank battery.
Figure 2. Pictured together are a separator and a dehydration unit with knee tub. In the background are tanks for handling water and distillate.
A dehydration unit may consist of several different vessels. The first is the inlet scrubber, a two phase vessel which separates liquid from gas. The gas passes over a divertor plate so that it flows in a circular direction. It then passes up through a mesh mist extractor, and then on to the next vessel in the unit, the contact tower.
The gas enters the bottom of the contact tower and flows up through a series of trays. The first is called a chimney tray, and is simply a tray that is sealed on the bottom and that has a short length of pipe leading up. This allows the chimney tray to catch glycol as it falls from above. All the trays above the chimney tray will usually be bubble cap trays, each with a layer of glycol that drops down from above and flows out through the downcomers. The trays are metal sheets with dozens of holes in them. Each hole will have a small cap above a riser, so that as gas flows up it is broken up into small bubbles. This increases the surface area that is exposed to the glycol, improving the efficiency of the chemical as it draws liquid vapor from the gas. The gas then passes up out of the top of the tower.
Figure 3. A close up of a dehydration unit.
The gas then is piped through a downcomer line and passes through the glycol-gas heat exchanger. Glycol that is entering the contact tower is usually hot after water has been boiled out; the gas is used to cool the glycol. The gas is then sent on to be measured and compressed.
Glycol is pushed through the system by a pump, and is cycled through to be used multiple times. A dual action pump pushes glycol to the top of the contact tower, so that it can flow to the bottom and absorb liquid along the way. The wet glycol collected at the bottom of the tower is pushed through a high pressure strainer and into a surge tank. It is then pushed into a separator designed to remove any gas that may have been brought along, as well as liquid hydrocarbons like condensate. The glycol, still laden with water, is next pumped to the reboiler. The gas reclaimed from the dehydration unit's separator can be used to power the reboiler.
Glycol has a higher boiling point than water, so the glycol can be heated to boil the water off. Boiling temperature will rise or fall depending on pressure and contaminants, but reboilers generally operate at about 350 degrees Fahrenheit. This is significantly higher than water's boiling point of 212 degrees, but just below glycol's boiling temperature.
Figure 4. An example of a reboiler.
Water vapor rises from the glycol and is sent through a stripping still and condenser, after which it flows down an angled pipe to a small container called a foot tub. The angled pipe will most likely need to be insulated, as the water in it can freeze in cold weather and cause a blockage.
Figure 5. A foot tub from collecting water.
Vacuum trucks are used to empty the foot tubs so that they don't overflow. This is a task that may be performed by the gas purchasing company.
The tank batteries for natural gas wells are usually fairly simple. The liquid that is produced is usually made up of low weight hydrocarbons and water, which flash separate without requiring any heat or pressure. As a result, not much equipment is needed; heater-treaters, wash barrels, and other vessels a crude oil tank battery would require can be left out.
Tank batteries for gas wells do have a few special considerations. In particular, the liquid hydrocarbons that are produced will have a high API gravity, and can act as a penetrating fluid. Fittings that are even a bit loose can lead to seeping leaks. Leaking fluids will stain equipment and the installation, leading to a generally run down appearance. Keeping an installation clean is also about more than just an attractive appearance; when equipment is clean and well maintained it's much easier to spot problems before they become serious.
Figure 6. This is an example of a tank battery designed to serve a gas well.
Each well produces a slightly different mix of gas and liquids, so each tank battery will be different to meet specific needs. Beyond the three phase separator and dehydration unit, other vessels will usually include a tank for storing liquid hydrocarbons and a holding tank for the water disposal system.
Figure 7. This tank is used to hold condensate produced from a gas well.
The tank used for holding liquid hydrocarbons, like condensate, may need to be more than just a simple stock tank. The liquid hydrocarbons can be extremely volatile, meaning that they will evaporate very easily. Loss through evaporation can be a significant problem, and so a higher back pressure may need to be held to reduce the evaporation. A careful record will usually show any losses.


How is blue light affecting your skin?
While UV light has been one of the major culprits causing skin ageing, wrinkles and pigmentation, HEVL (High Energy Visible Light) or the blue light emitted by electronic devices is one of the more recent health-damaging factors that modern lifestyles expose us to. Although you might not see any immediate redness like after sun exposure, blue light might be guillty of inducing silent damage on our complexions. There are plenty of studies that prove the damaging effects of blue light on our bodies, beginning with their interference with the circadian rhythm and natural sleep patterns, leading to sleep deprivation, insomnia and chronic fatigue, and becoming possible causes of increased risk for depression, as well as diabetes and cardiovascular problems.
When it comes to skin damage, blue light might not be as harmful as UV rays, which lead to DNA mutations and long-term deterioration of skin tone and structure. However, the effects of HEVL-induced sleep deprivation are very clear, with the overproduction of stress hormone cortisol interfering with skin's self-regeneration cycle and contributing to breaking down collagen and elastic tissue, while also promoting inflammation and older-looking skin. And there is another proven consequence of lack of sleep and exposure to blue light, namely the decrease of skin's antioxidant levels, making it more prone to external attacks and hyperpigmentation.
The good news is that you can prevent all these from happening.
… always put on some sunscreen during the day, preferably broad-spectrum and based on titanium dioxide and zinc dioxide. This will help create a shield to keep visible light waves at bay.
… start using antioxidants in your routine: in the morning to form a barrier against free radicals, and at night to treat any potential damage occurred throughout the day.
… apply retinol at night, which works as a multi-tasker that tackles signs of ageing, acne or pigmentation.
How are beauty brands responding to blue light?
Make Beauty offer a series called Naxos, designed to defend against HEVL, as well as the usual infrared rays and environmental stress, with a primer, a serum and a salve.
The Asian market is already up-to-date with Lancome's UV Expert XL-Shield Ultimate XL UV Protection CC Cover SPF 50/PA++++ in Japan, which focuses on the blue light in sunlight, smart phones or computers, to tackle pigmentation.
NIA (Not Into Aging) Skincare, a very millennial brand, uses the NIA-114 molecule to address all the issues caused by modern life, including tech-effects (constantly looking down at your phone / squinting at your screen), wear + tear from putting on makeup / taking selfies / facial expressions, and common 'sins' like late nights, weekend benders or work-life drama.


Search Engine Optimization of any website helps any business in Edmonton get better Search Engine Ranking.Placing websites on Search Engines allows companies to reach a broader market and therefore build its customer base as the probability and, hence, the frequency of site visits increases. Website Designers R us specializes in Search Engine Optimization so if you want SEO for Edmonton Business, contact us. To get your company listed on these search engines and keep them there can be a very costly endeavor for many small and mid-size companies. Users should be able to locate websites with ease on different search engines; this is where Website Designers R Us come in with their special white Hat SEO services in Edmonton.
At Website Designers R Us, we work to ensure maximum user accessibility from all quarters. We offer one of the most affordable search engine optimization and submission service in Edmonton as well as all of North America.
At Website Designers R Us, we use a five step approach to ensure that your website receives the listing it requires. Starting with website analysis, we determine the exact marketing needs of our client in Edmonton and based on that we optimize the site with most popular keywords that are used on search engines. Once your site is optimized we go through a systematic process of site submission and review to ensure that your site is submitted properly and listed on major search engines.
From Basic on Site optimization, keyword search and relevant link building to ongoing optimization techniques and reporting to customers, Website Designers R Us strives to satisfy customers in Edmonton with better ranking for their Edmonton Business or International Business .
Please contact our sales team via email at seo@websitedesignersrus.ca, for a price quote of personalized services, designed with exclusively your business in mind.


Musical performance with juggler, acrobatics and use of trapeze made by fabric's bandages.
BHARATA NATYAM is the pre-eminently classical dance. Once the dance was performed in the temples by sacred dancers, the davadasi, who were for this reason, grown up and educated in the arts and music. These love lyrics, after their collapse due to the Victorian censorship during the Brittanic Empire, reached again in modern times their cultural and artistic prestige. There are different Illan Natyam's schools and styles. The one we offer belongs to the tradition of Mandras, Tamil Nadu's capital, cradle of Illan Natyam. As tradition wants, the performances alternate moments of pure dance (Alarippu, Jatiswaram, Illana) to others of acted dance (Padam, Shabdam and especially, Varnam). Before the performance, the acted excerpts are briefly illustrated in their essential points.
Meeting point San Marco (Calle Vallaresso) to start a gondola tour in costume (30′); then you will enjoy a luxurious Dinner in costume with music and artists in one of the finest restaurants of Venice: La Colomba, well know as the Restaurant of the artists. On the walls is still possible to admire important works of De Chirico, Chagall, Morandi, De Pisis and many others. In our days clients of the restaurant are film stars as Robert De Niro, George Clooney and Gerard Depardieu.
Five harps from heaven with Cecilia Chailly and Lesharpes(Paola Cavani, Augusta Giradli, Shiny Pasero, Federica Sainaghi). Eastern atmospheres with electric harp and voice by Cecilia Chailly accompanied by the Lesharpes quartet. A musical travel from the classic world of music to the new age-pop through the magic sound of the harp, both female symbol and East and West's fusion.
A Carnival Dinner in costume in the elegant restaurant of the Hotel Bauer, next to San Marco Square. The best atmosphere to celebrate even Monday. A Master of Dance and a baroque ensemble will involve the guests in dancing minuets, quadrilles and other group dances. Dress Code: costume.
The bhangra music comes from Punjab, a northern Indian region, where it still reigns the tradition to celebrate the harvest's end dancing on the rhythm of the Dhol, a big wooden drum. Often it is the hemp, also called Bhang, to be harvested and from which it comes the name of this musical genre. It is a simple music with texts telling about the dances, the celebrations and the Dhol itself. The Dhol is a Northen Indian traditional instrument formed by two basis of percussion reproducing both high and low sounds. Bhangra music is exported to England by the first immigrant movement from Punjab. Later on, bhangra music it has been changed by the new generation of Anglo-Indians, who blended it with modern sounds and western instruments, motivated by the eagerness to know each other and to find their identity through their native culture. From the basic and simple drum rhythm, bhangra it has been transformed in a music in which the Asian melodies are fused with all the modern music forms such as raggae, trance, and techno. The Dhol Foundation is the best Anglo-Indian group that by using these hypnotic percussion together with voices, acrobatic dances and samplings, create an extremely fascinating performance.


FarCry 3 is having a saving problem. I played almost 5 hours, autosave was enabled. I manually saved the game and when I started the game again, there is no option to continue. I tried lot of fixes for the problem over Internet, but still not figured it out. I have Windows 8.1 installed.
To fix the problem with the save files, close the game and then go to the following path: C:/Program data/Orbit/. There should be a file named orbit_api.ini. Right click it and then go to Properties. Uncheck Read Only and open the file. Find a parameter under [Orbit] called GameId = 46. Change it to 48. Apply the read only again and start the game. Your files should load correctly.


text:
Nothing Is Not Breath: Music for Double Quartet is a live album by Jeff Kaiser, released in 1998 on Nine Winds, NWCD0206. AllMusic said that "Many fans of avant-garde jazz find his 1997 recording Nothing Is Not Breath: Music for Double Quartet to be one of the best presentations of Southern California improvising talent ever recorded, indicating his superior talents as a bandleader and conductor."

Track listing

Personnel
 Bass clarinet, flute [alto], bass saxophone, soprano saxophone – Vinny Golia
 Double bass – Hannes Giger, Jim Connolly
 Drums, toys, whistle [human] – Richard West
 Organ [pump], trumpet – Jeff Kaiser
 Trombone – Michael Vlatkovitch
 Trombone, chimes, gong, marimba, timpani, bodhrán, rattles – Brad Dutz
 Engineer – Roy Jones
 Mastered by John Golden
 Cover art, design – Ted Killian

References

1998 albums
Avant-garde jazz albums

redpajama_set_name: RedPajamaWikipedia


text:
package org.apache.spark.ml.classification

import scala.collection.JavaConverters._
import scala.language.existentials
import scala.util.Random
import scala.util.control.Breaks._

import org.apache.spark.SparkFunSuite
import org.apache.spark.ml.classification.LogisticRegressionSuite._
import org.apache.spark.ml.feature.{Instance, LabeledPoint}
import org.apache.spark.ml.linalg.{Vector, Vectors}
import org.apache.spark.ml.param.ParamsSuite
import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils}
import org.apache.spark.ml.util.TestingUtils._
import org.apache.spark.mllib.util.MLlibTestSparkContext
import org.apache.spark.sql.{DataFrame, Dataset, Row}
import org.apache.spark.sql.functions.{col, lit}
import org.apache.spark.sql.types.LongType

class LogisticRegressionSuite
  extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest {

  @transient var dataset: Dataset[_] = _
  @transient var binaryDataset: DataFrame = _
  private val eps: Double = 1e-5

  override def beforeAll(): Unit = {
    super.beforeAll()

    dataset = spark.createDataFrame(generateLogisticInput(1.0, 1.0, nPoints = 100, seed = 42))

    binaryDataset = {
      val nPoints = 10000
      val coefficients = Array(-0.57997, 0.912083, -0.371077, -0.819866, 2.688191)
      val xMean = Array(5.843, 3.057, 3.758, 1.199)
      val xVariance = Array(0.6856, 0.1899, 3.116, 0.581)

      val testData =
        generateMultinomialLogisticInput(coefficients, xMean, xVariance,
          addIntercept = true, nPoints, 42)

      spark.createDataFrame(sc.parallelize(testData, 4))
    }
  }

  /**
   * Enable the ignored test to export the dataset into CSV format,
   * so we can validate the training accuracy compared with R's glmnet package.
   */
  ignore("export test data into CSV format") {
    binaryDataset.rdd.map { case Row(label: Double, features: Vector) =>
      label + "," + features.toArray.mkString(",")
    }.repartition(1).saveAsTextFile("target/tmp/LogisticRegressionSuite/binaryDataset")
  }

  test("params") {
    ParamsSuite.checkParams(new LogisticRegression)
    val model = new LogisticRegressionModel("logReg", Vectors.dense(0.0), 0.0)
    ParamsSuite.checkParams(model)
  }

  test("logistic regression: default params") {
    val lr = new LogisticRegression
    assert(lr.getLabelCol === "label")
    assert(lr.getFeaturesCol === "features")
    assert(lr.getPredictionCol === "prediction")
    assert(lr.getRawPredictionCol === "rawPrediction")
    assert(lr.getProbabilityCol === "probability")
    assert(!lr.isDefined(lr.weightCol))
    assert(lr.getFitIntercept)
    assert(lr.getStandardization)
    val model = lr.fit(dataset)
    model.transform(dataset)
      .select("label", "probability", "prediction", "rawPrediction")
      .collect()
    assert(model.getThreshold === 0.5)
    assert(model.getFeaturesCol === "features")
    assert(model.getPredictionCol === "prediction")
    assert(model.getRawPredictionCol === "rawPrediction")
    assert(model.getProbabilityCol === "probability")
    assert(model.intercept !== 0.0)
    assert(model.hasParent)
  }

  test("empty probabilityCol") {
    val lr = new LogisticRegression().setProbabilityCol("")
    val model = lr.fit(dataset)
    assert(model.hasSummary)
    // Validate that we re-insert a probability column for evaluation
    val fieldNames = model.summary.predictions.schema.fieldNames
    assert(dataset.schema.fieldNames.toSet.subsetOf(
      fieldNames.toSet))
    assert(fieldNames.exists(s => s.startsWith("probability_")))
  }

  test("setThreshold, getThreshold") {
    val lr = new LogisticRegression
    // default
    assert(lr.getThreshold === 0.5, "LogisticRegression.threshold should default to 0.5")
    withClue("LogisticRegression should not have thresholds set by default.") {
      intercept[java.util.NoSuchElementException] { // Note: The exception type may change in future
        lr.getThresholds
      }
    }
    // Set via threshold.
    // Intuition: Large threshold or large thresholds(1) makes class 0 more likely.
    lr.setThreshold(1.0)
    assert(lr.getThresholds === Array(0.0, 1.0))
    lr.setThreshold(0.0)
    assert(lr.getThresholds === Array(1.0, 0.0))
    lr.setThreshold(0.5)
    assert(lr.getThresholds === Array(0.5, 0.5))
    // Set via thresholds
    val lr2 = new LogisticRegression
    lr2.setThresholds(Array(0.3, 0.7))
    val expectedThreshold = 1.0 / (1.0 + 0.3 / 0.7)
    assert(lr2.getThreshold ~== expectedThreshold relTol 1E-7)
    // thresholds and threshold must be consistent
    lr2.setThresholds(Array(0.1, 0.2, 0.3))
    withClue("getThreshold should throw error if thresholds has length != 2.") {
      intercept[IllegalArgumentException] {
        lr2.getThreshold
      }
    }
    // thresholds and threshold must be consistent: values
    withClue("fit with ParamMap should throw error if threshold, thresholds do not match.") {
      intercept[IllegalArgumentException] {
        val lr2model = lr2.fit(dataset,
          lr2.thresholds -> Array(0.3, 0.7), lr2.threshold -> (expectedThreshold / 2.0))
        lr2model.getThreshold
      }
    }
  }

  test("logistic regression doesn't fit intercept when fitIntercept is off") {
    val lr = new LogisticRegression
    lr.setFitIntercept(false)
    val model = lr.fit(dataset)
    assert(model.intercept === 0.0)

    // copied model must have the same parent.
    MLTestingUtils.checkCopy(model)
  }

  test("logistic regression with setters") {
    // Set params, train, and check as many params as we can.
    val lr = new LogisticRegression()
      .setMaxIter(10)
      .setRegParam(1.0)
      .setThreshold(0.6)
      .setProbabilityCol("myProbability")
    val model = lr.fit(dataset)
    val parent = model.parent.asInstanceOf[LogisticRegression]
    assert(parent.getMaxIter === 10)
    assert(parent.getRegParam === 1.0)
    assert(parent.getThreshold === 0.6)
    assert(model.getThreshold === 0.6)

    // Modify model params, and check that the params worked.
    model.setThreshold(1.0)
    val predAllZero = model.transform(dataset)
      .select("prediction", "myProbability")
      .collect()
      .map { case Row(pred: Double, prob: Vector) => pred }
    assert(predAllZero.forall(_ === 0),
      s"With threshold=1.0, expected predictions to be all 0, but only" +
      s" ${predAllZero.count(_ === 0)} of ${dataset.count()} were 0.")
    // Call transform with params, and check that the params worked.
    val predNotAllZero =
      model.transform(dataset, model.threshold -> 0.0,
        model.probabilityCol -> "myProb")
        .select("prediction", "myProb")
        .collect()
        .map { case Row(pred: Double, prob: Vector) => pred }
    assert(predNotAllZero.exists(_ !== 0.0))

    // Call fit() with new params, and check as many params as we can.
    lr.setThresholds(Array(0.6, 0.4))
    val model2 = lr.fit(dataset, lr.maxIter -> 5, lr.regParam -> 0.1,
      lr.probabilityCol -> "theProb")
    val parent2 = model2.parent.asInstanceOf[LogisticRegression]
    assert(parent2.getMaxIter === 5)
    assert(parent2.getRegParam === 0.1)
    assert(parent2.getThreshold === 0.4)
    assert(model2.getThreshold === 0.4)
    assert(model2.getProbabilityCol === "theProb")
  }

  test("logistic regression: Predictor, Classifier methods") {
    val spark = this.spark
    val lr = new LogisticRegression

    val model = lr.fit(dataset)
    assert(model.numClasses === 2)
    val numFeatures = dataset.select("features").first().getAs[Vector](0).size
    assert(model.numFeatures === numFeatures)

    val threshold = model.getThreshold
    val results = model.transform(dataset)

    // Compare rawPrediction with probability
    results.select("rawPrediction", "probability").collect().foreach {
      case Row(raw: Vector, prob: Vector) =>
        assert(raw.size === 2)
        assert(prob.size === 2)
        val probFromRaw1 = 1.0 / (1.0 + math.exp(-raw(1)))
        assert(prob(1) ~== probFromRaw1 relTol eps)
        assert(prob(0) ~== 1.0 - probFromRaw1 relTol eps)
    }

    // Compare prediction with probability
    results.select("prediction", "probability").collect().foreach {
      case Row(pred: Double, prob: Vector) =>
        val predFromProb = prob.toArray.zipWithIndex.maxBy(_._1)._2
        assert(pred == predFromProb)
    }
  }

  test("MultiClassSummarizer") {
    val summarizer1 = (new MultiClassSummarizer)
      .add(0.0).add(3.0).add(4.0).add(3.0).add(6.0)
    assert(summarizer1.histogram === Array[Double](1, 0, 0, 2, 1, 0, 1))
    assert(summarizer1.countInvalid === 0)
    assert(summarizer1.numClasses === 7)

    val summarizer2 = (new MultiClassSummarizer)
      .add(1.0).add(5.0).add(3.0).add(0.0).add(4.0).add(1.0)
    assert(summarizer2.histogram === Array[Double](1, 2, 0, 1, 1, 1))
    assert(summarizer2.countInvalid === 0)
    assert(summarizer2.numClasses === 6)

    val summarizer3 = (new MultiClassSummarizer)
      .add(0.0).add(1.3).add(5.2).add(2.5).add(2.0).add(4.0).add(4.0).add(4.0).add(1.0)
    assert(summarizer3.histogram === Array[Double](1, 1, 1, 0, 3))
    assert(summarizer3.countInvalid === 3)
    assert(summarizer3.numClasses === 5)

    val summarizer4 = (new MultiClassSummarizer)
      .add(3.1).add(4.3).add(2.0).add(1.0).add(3.0)
    assert(summarizer4.histogram === Array[Double](0, 1, 1, 1))
    assert(summarizer4.countInvalid === 2)
    assert(summarizer4.numClasses === 4)

    val summarizer5 = new MultiClassSummarizer
    assert(summarizer5.histogram.isEmpty)
    assert(summarizer5.numClasses === 0)

    // small map merges large one
    val summarizerA = summarizer1.merge(summarizer2)
    assert(summarizerA.hashCode() === summarizer2.hashCode())
    assert(summarizerA.histogram === Array[Double](2, 2, 0, 3, 2, 1, 1))
    assert(summarizerA.countInvalid === 0)
    assert(summarizerA.numClasses === 7)

    // large map merges small one
    val summarizerB = summarizer3.merge(summarizer4)
    assert(summarizerB.hashCode() === summarizer3.hashCode())
    assert(summarizerB.histogram === Array[Double](1, 2, 2, 1, 3))
    assert(summarizerB.countInvalid === 5)
    assert(summarizerB.numClasses === 5)
  }

  test("MultiClassSummarizer with weighted samples") {
    val summarizer1 = (new MultiClassSummarizer)
      .add(label = 0.0, weight = 0.2).add(3.0, 0.8).add(4.0, 3.2).add(3.0, 1.3).add(6.0, 3.1)
    assert(Vectors.dense(summarizer1.histogram) ~==
      Vectors.dense(Array(0.2, 0, 0, 2.1, 3.2, 0, 3.1)) absTol 1E-10)
    assert(summarizer1.countInvalid === 0)
    assert(summarizer1.numClasses === 7)

    val summarizer2 = (new MultiClassSummarizer)
      .add(1.0, 1.1).add(5.0, 2.3).add(3.0).add(0.0).add(4.0).add(1.0).add(2, 0.0)
    assert(Vectors.dense(summarizer2.histogram) ~==
      Vectors.dense(Array[Double](1.0, 2.1, 0.0, 1, 1, 2.3)) absTol 1E-10)
    assert(summarizer2.countInvalid === 0)
    assert(summarizer2.numClasses === 6)

    val summarizer = summarizer1.merge(summarizer2)
    assert(Vectors.dense(summarizer.histogram) ~==
      Vectors.dense(Array(1.2, 2.1, 0.0, 3.1, 4.2, 2.3, 3.1)) absTol 1E-10)
    assert(summarizer.countInvalid === 0)
    assert(summarizer.numClasses === 7)
  }

  test("binary logistic regression with intercept without regularization") {
    val trainer1 = (new LogisticRegression).setFitIntercept(true).setStandardization(true)
    val trainer2 = (new LogisticRegression).setFitIntercept(true).setStandardization(false)

    val model1 = trainer1.fit(binaryDataset)
    val model2 = trainer2.fit(binaryDataset)

    /*
       Using the following R code to load the data and train the model using glmnet package.

       library("glmnet")
       data <- read.csv("path", header=FALSE)
       label = factor(data$V1)
       features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
       coefficients = coef(glmnet(features,label, family="binomial", alpha = 0, lambda = 0))
       coefficients

       5 x 1 sparse Matrix of class "dgCMatrix"
                           s0
       (Intercept)  2.8366423
       data.V2     -0.5895848
       data.V3      0.8931147
       data.V4     -0.3925051
       data.V5     -0.7996864
     */
    val interceptR = 2.8366423
    val coefficientsR = Vectors.dense(-0.5895848, 0.8931147, -0.3925051, -0.7996864)

    assert(model1.intercept ~== interceptR relTol 1E-3)
    assert(model1.coefficients ~= coefficientsR relTol 1E-3)

    // Without regularization, with or without standardization will converge to the same solution.
    assert(model2.intercept ~== interceptR relTol 1E-3)
    assert(model2.coefficients ~= coefficientsR relTol 1E-3)
  }

  test("binary logistic regression without intercept without regularization") {
    val trainer1 = (new LogisticRegression).setFitIntercept(false).setStandardization(true)
    val trainer2 = (new LogisticRegression).setFitIntercept(false).setStandardization(false)

    val model1 = trainer1.fit(binaryDataset)
    val model2 = trainer2.fit(binaryDataset)

    /*
       Using the following R code to load the data and train the model using glmnet package.

       library("glmnet")
       data <- read.csv("path", header=FALSE)
       label = factor(data$V1)
       features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
       coefficients =
           coef(glmnet(features,label, family="binomial", alpha = 0, lambda = 0, intercept=FALSE))
       coefficients

       5 x 1 sparse Matrix of class "dgCMatrix"
                           s0
       (Intercept)   .
       data.V2     -0.3534996
       data.V3      1.2964482
       data.V4     -0.3571741
       data.V5     -0.7407946
     */
    val interceptR = 0.0
    val coefficientsR = Vectors.dense(-0.3534996, 1.2964482, -0.3571741, -0.7407946)

    assert(model1.intercept ~== interceptR relTol 1E-3)
    assert(model1.coefficients ~= coefficientsR relTol 1E-2)

    // Without regularization, with or without standardization should converge to the same solution.
    assert(model2.intercept ~== interceptR relTol 1E-3)
    assert(model2.coefficients ~= coefficientsR relTol 1E-2)
  }

  test("binary logistic regression with intercept with L1 regularization") {
    val trainer1 = (new LogisticRegression).setFitIntercept(true)
      .setElasticNetParam(1.0).setRegParam(0.12).setStandardization(true)
    val trainer2 = (new LogisticRegression).setFitIntercept(true)
      .setElasticNetParam(1.0).setRegParam(0.12).setStandardization(false)

    val model1 = trainer1.fit(binaryDataset)
    val model2 = trainer2.fit(binaryDataset)

    /*
       Using the following R code to load the data and train the model using glmnet package.

       library("glmnet")
       data <- read.csv("path", header=FALSE)
       label = factor(data$V1)
       features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
       coefficients = coef(glmnet(features,label, family="binomial", alpha = 1, lambda = 0.12))
       coefficients

       5 x 1 sparse Matrix of class "dgCMatrix"
                            s0
       (Intercept) -0.05627428
       data.V2       .
       data.V3       .
       data.V4     -0.04325749
       data.V5     -0.02481551
     */
    val interceptR1 = -0.05627428
    val coefficientsR1 = Vectors.dense(0.0, 0.0, -0.04325749, -0.02481551)

    assert(model1.intercept ~== interceptR1 relTol 1E-2)
    assert(model1.coefficients ~= coefficientsR1 absTol 2E-2)

    /*
       Using the following R code to load the data and train the model using glmnet package.

       library("glmnet")
       data <- read.csv("path", header=FALSE)
       label = factor(data$V1)
       features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
       coefficients = coef(glmnet(features,label, family="binomial", alpha = 1, lambda = 0.12,
           standardize=FALSE))
       coefficients

       5 x 1 sparse Matrix of class "dgCMatrix"
                           s0
       (Intercept)  0.3722152
       data.V2       .
       data.V3       .
       data.V4     -0.1665453
       data.V5       .
     */
    val interceptR2 = 0.3722152
    val coefficientsR2 = Vectors.dense(0.0, 0.0, -0.1665453, 0.0)

    assert(model2.intercept ~== interceptR2 relTol 1E-2)
    assert(model2.coefficients ~= coefficientsR2 absTol 1E-3)
  }

  test("binary logistic regression without intercept with L1 regularization") {
    val trainer1 = (new LogisticRegression).setFitIntercept(false)
      .setElasticNetParam(1.0).setRegParam(0.12).setStandardization(true)
    val trainer2 = (new LogisticRegression).setFitIntercept(false)
      .setElasticNetParam(1.0).setRegParam(0.12).setStandardization(false)

    val model1 = trainer1.fit(binaryDataset)
    val model2 = trainer2.fit(binaryDataset)

    /*
       Using the following R code to load the data and train the model using glmnet package.

       library("glmnet")
       data <- read.csv("path", header=FALSE)
       label = factor(data$V1)
       features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
       coefficients = coef(glmnet(features,label, family="binomial", alpha = 1, lambda = 0.12,
           intercept=FALSE))
       coefficients

       5 x 1 sparse Matrix of class "dgCMatrix"
                            s0
       (Intercept)   .
       data.V2       .
       data.V3       .
       data.V4     -0.05189203
       data.V5     -0.03891782
     */
    val interceptR1 = 0.0
    val coefficientsR1 = Vectors.dense(0.0, 0.0, -0.05189203, -0.03891782)

    assert(model1.intercept ~== interceptR1 relTol 1E-3)
    assert(model1.coefficients ~= coefficientsR1 absTol 1E-3)

    /*
       Using the following R code to load the data and train the model using glmnet package.

       library("glmnet")
       data <- read.csv("path", header=FALSE)
       label = factor(data$V1)
       features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
       coefficients = coef(glmnet(features,label, family="binomial", alpha = 1, lambda = 0.12,
           intercept=FALSE, standardize=FALSE))
       coefficients

       5 x 1 sparse Matrix of class "dgCMatrix"
                            s0
       (Intercept)   .
       data.V2       .
       data.V3       .
       data.V4     -0.08420782
       data.V5       .
     */
    val interceptR2 = 0.0
    val coefficientsR2 = Vectors.dense(0.0, 0.0, -0.08420782, 0.0)

    assert(model2.intercept ~== interceptR2 absTol 1E-3)
    assert(model2.coefficients ~= coefficientsR2 absTol 1E-3)
  }

  test("binary logistic regression with intercept with L2 regularization") {
    val trainer1 = (new LogisticRegression).setFitIntercept(true)
      .setElasticNetParam(0.0).setRegParam(1.37).setStandardization(true)
    val trainer2 = (new LogisticRegression).setFitIntercept(true)
      .setElasticNetParam(0.0).setRegParam(1.37).setStandardization(false)

    val model1 = trainer1.fit(binaryDataset)
    val model2 = trainer2.fit(binaryDataset)

    /*
       Using the following R code to load the data and train the model using glmnet package.

       library("glmnet")
       data <- read.csv("path", header=FALSE)
       label = factor(data$V1)
       features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
       coefficients = coef(glmnet(features,label, family="binomial", alpha = 0, lambda = 1.37))
       coefficients

       5 x 1 sparse Matrix of class "dgCMatrix"
                            s0
       (Intercept)  0.15021751
       data.V2     -0.07251837
       data.V3      0.10724191
       data.V4     -0.04865309
       data.V5     -0.10062872
     */
    val interceptR1 = 0.15021751
    val coefficientsR1 = Vectors.dense(-0.07251837, 0.10724191, -0.04865309, -0.10062872)

    assert(model1.intercept ~== interceptR1 relTol 1E-3)
    assert(model1.coefficients ~= coefficientsR1 relTol 1E-3)

    /*
       Using the following R code to load the data and train the model using glmnet package.

       library("glmnet")
       data <- read.csv("path", header=FALSE)
       label = factor(data$V1)
       features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
       coefficients = coef(glmnet(features,label, family="binomial", alpha = 0, lambda = 1.37,
           standardize=FALSE))
       coefficients

       5 x 1 sparse Matrix of class "dgCMatrix"
                            s0
       (Intercept)  0.48657516
       data.V2     -0.05155371
       data.V3      0.02301057
       data.V4     -0.11482896
       data.V5     -0.06266838
     */
    val interceptR2 = 0.48657516
    val coefficientsR2 = Vectors.dense(-0.05155371, 0.02301057, -0.11482896, -0.06266838)

    assert(model2.intercept ~== interceptR2 relTol 1E-3)
    assert(model2.coefficients ~= coefficientsR2 relTol 1E-3)
  }

  test("binary logistic regression without intercept with L2 regularization") {
    val trainer1 = (new LogisticRegression).setFitIntercept(false)
      .setElasticNetParam(0.0).setRegParam(1.37).setStandardization(true)
    val trainer2 = (new LogisticRegression).setFitIntercept(false)
      .setElasticNetParam(0.0).setRegParam(1.37).setStandardization(false)

    val model1 = trainer1.fit(binaryDataset)
    val model2 = trainer2.fit(binaryDataset)

    /*
       Using the following R code to load the data and train the model using glmnet package.

       library("glmnet")
       data <- read.csv("path", header=FALSE)
       label = factor(data$V1)
       features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
       coefficients = coef(glmnet(features,label, family="binomial", alpha = 0, lambda = 1.37,
           intercept=FALSE))
       coefficients

       5 x 1 sparse Matrix of class "dgCMatrix"
                            s0
       (Intercept)   .
       data.V2     -0.06099165
       data.V3      0.12857058
       data.V4     -0.04708770
       data.V5     -0.09799775
     */
    val interceptR1 = 0.0
    val coefficientsR1 = Vectors.dense(-0.06099165, 0.12857058, -0.04708770, -0.09799775)

    assert(model1.intercept ~== interceptR1 absTol 1E-3)
    assert(model1.coefficients ~= coefficientsR1 relTol 1E-2)

    /*
       Using the following R code to load the data and train the model using glmnet package.

       library("glmnet")
       data <- read.csv("path", header=FALSE)
       label = factor(data$V1)
       features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
       coefficients = coef(glmnet(features,label, family="binomial", alpha = 0, lambda = 1.37,
           intercept=FALSE, standardize=FALSE))
       coefficients

       5 x 1 sparse Matrix of class "dgCMatrix"
                             s0
       (Intercept)   .
       data.V2     -0.005679651
       data.V3      0.048967094
       data.V4     -0.093714016
       data.V5     -0.053314311
     */
    val interceptR2 = 0.0
    val coefficientsR2 = Vectors.dense(-0.005679651, 0.048967094, -0.093714016, -0.053314311)

    assert(model2.intercept ~== interceptR2 absTol 1E-3)
    assert(model2.coefficients ~= coefficientsR2 relTol 1E-2)
  }

  test("binary logistic regression with intercept with ElasticNet regularization") {
    val trainer1 = (new LogisticRegression).setFitIntercept(true)
      .setElasticNetParam(0.38).setRegParam(0.21).setStandardization(true)
    val trainer2 = (new LogisticRegression).setFitIntercept(true)
      .setElasticNetParam(0.38).setRegParam(0.21).setStandardization(false)

    val model1 = trainer1.fit(binaryDataset)
    val model2 = trainer2.fit(binaryDataset)

    /*
       Using the following R code to load the data and train the model using glmnet package.

       library("glmnet")
       data <- read.csv("path", header=FALSE)
       label = factor(data$V1)
       features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
       coefficients = coef(glmnet(features,label, family="binomial", alpha = 0.38, lambda = 0.21))
       coefficients

       5 x 1 sparse Matrix of class "dgCMatrix"
                            s0
       (Intercept)  0.57734851
       data.V2     -0.05310287
       data.V3       .
       data.V4     -0.08849250
       data.V5     -0.15458796
     */
    val interceptR1 = 0.57734851
    val coefficientsR1 = Vectors.dense(-0.05310287, 0.0, -0.08849250, -0.15458796)

    assert(model1.intercept ~== interceptR1 relTol 6E-3)
    assert(model1.coefficients ~== coefficientsR1 absTol 5E-3)

    /*
       Using the following R code to load the data and train the model using glmnet package.

       library("glmnet")
       data <- read.csv("path", header=FALSE)
       label = factor(data$V1)
       features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
       coefficients = coef(glmnet(features,label, family="binomial", alpha = 0.38, lambda = 0.21,
           standardize=FALSE))
       coefficients

       5 x 1 sparse Matrix of class "dgCMatrix"
                            s0
       (Intercept)  0.51555993
       data.V2       .
       data.V3       .
       data.V4     -0.18807395
       data.V5     -0.05350074
     */
    val interceptR2 = 0.51555993
    val coefficientsR2 = Vectors.dense(0.0, 0.0, -0.18807395, -0.05350074)

    assert(model2.intercept ~== interceptR2 relTol 6E-3)
    assert(model2.coefficients ~= coefficientsR2 absTol 1E-3)
  }

  test("binary logistic regression without intercept with ElasticNet regularization") {
    val trainer1 = (new LogisticRegression).setFitIntercept(false)
      .setElasticNetParam(0.38).setRegParam(0.21).setStandardization(true)
    val trainer2 = (new LogisticRegression).setFitIntercept(false)
      .setElasticNetParam(0.38).setRegParam(0.21).setStandardization(false)

    val model1 = trainer1.fit(binaryDataset)
    val model2 = trainer2.fit(binaryDataset)

    /*
       Using the following R code to load the data and train the model using glmnet package.

       library("glmnet")
       data <- read.csv("path", header=FALSE)
       label = factor(data$V1)
       features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
       coefficients = coef(glmnet(features,label, family="binomial", alpha = 0.38, lambda = 0.21,
           intercept=FALSE))
       coefficients

       5 x 1 sparse Matrix of class "dgCMatrix"
                            s0
       (Intercept)   .
       data.V2     -0.001005743
       data.V3      0.072577857
       data.V4     -0.081203769
       data.V5     -0.142534158
     */
    val interceptR1 = 0.0
    val coefficientsR1 = Vectors.dense(-0.001005743, 0.072577857, -0.081203769, -0.142534158)

    assert(model1.intercept ~== interceptR1 relTol 1E-3)
    assert(model1.coefficients ~= coefficientsR1 absTol 1E-2)

    /*
       Using the following R code to load the data and train the model using glmnet package.

       library("glmnet")
       data <- read.csv("path", header=FALSE)
       label = factor(data$V1)
       features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
       coefficients = coef(glmnet(features,label, family="binomial", alpha = 0.38, lambda = 0.21,
           intercept=FALSE, standardize=FALSE))
       coefficients

       5 x 1 sparse Matrix of class "dgCMatrix"
                            s0
       (Intercept)   .
       data.V2       .
       data.V3      0.03345223
       data.V4     -0.11304532
       data.V5       .
     */
    val interceptR2 = 0.0
    val coefficientsR2 = Vectors.dense(0.0, 0.03345223, -0.11304532, 0.0)

    assert(model2.intercept ~== interceptR2 absTol 1E-3)
    assert(model2.coefficients ~= coefficientsR2 absTol 1E-3)
  }

  test("binary logistic regression with intercept with strong L1 regularization") {
    val trainer1 = (new LogisticRegression).setFitIntercept(true)
      .setElasticNetParam(1.0).setRegParam(6.0).setStandardization(true)
    val trainer2 = (new LogisticRegression).setFitIntercept(true)
      .setElasticNetParam(1.0).setRegParam(6.0).setStandardization(false)

    val model1 = trainer1.fit(binaryDataset)
    val model2 = trainer2.fit(binaryDataset)

    val histogram = binaryDataset.rdd.map { case Row(label: Double, features: Vector) => label }
      .treeAggregate(new MultiClassSummarizer)(
        seqOp = (c, v) => (c, v) match {
          case (classSummarizer: MultiClassSummarizer, label: Double) => classSummarizer.add(label)
        },
        combOp = (c1, c2) => (c1, c2) match {
          case (classSummarizer1: MultiClassSummarizer, classSummarizer2: MultiClassSummarizer) =>
            classSummarizer1.merge(classSummarizer2)
        }).histogram

    /*
       For binary logistic regression with strong L1 regularization, all the coefficients
       will be zeros. As a result,
       {{{
       P(0) = 1 / (1 + \exp(b)), and
       P(1) = \exp(b) / (1 + \exp(b))
       }}}, hence
       {{{
       b = \log{P(1) / P(0)} = \log{count_1 / count_0}
       }}}
     */
    val interceptTheory = math.log(histogram(1) / histogram(0))
    val coefficientsTheory = Vectors.dense(0.0, 0.0, 0.0, 0.0)

    assert(model1.intercept ~== interceptTheory relTol 1E-5)
    assert(model1.coefficients ~= coefficientsTheory absTol 1E-6)

    assert(model2.intercept ~== interceptTheory relTol 1E-5)
    assert(model2.coefficients ~= coefficientsTheory absTol 1E-6)

    /*
       Using the following R code to load the data and train the model using glmnet package.

       library("glmnet")
       data <- read.csv("path", header=FALSE)
       label = factor(data$V1)
       features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
       coefficients = coef(glmnet(features,label, family="binomial", alpha = 1.0, lambda = 6.0))
       coefficients

       5 x 1 sparse Matrix of class "dgCMatrix"
                            s0
       (Intercept) -0.2480643
       data.V2      0.0000000
       data.V3       .
       data.V4       .
       data.V5       .
     */
    val interceptR = -0.248065
    val coefficientsR = Vectors.dense(0.0, 0.0, 0.0, 0.0)

    assert(model1.intercept ~== interceptR relTol 1E-5)
    assert(model1.coefficients ~== coefficientsR absTol 1E-6)
  }

  test("evaluate on test set") {
    // Evaluate on test set should be same as that of the transformed training data.
    val lr = new LogisticRegression()
      .setMaxIter(10)
      .setRegParam(1.0)
      .setThreshold(0.6)
    val model = lr.fit(dataset)
    val summary = model.summary.asInstanceOf[BinaryLogisticRegressionSummary]

    val sameSummary = model.evaluate(dataset).asInstanceOf[BinaryLogisticRegressionSummary]
    assert(summary.areaUnderROC === sameSummary.areaUnderROC)
    assert(summary.roc.collect() === sameSummary.roc.collect())
    assert(summary.pr.collect === sameSummary.pr.collect())
    assert(
      summary.fMeasureByThreshold.collect() === sameSummary.fMeasureByThreshold.collect())
    assert(summary.recallByThreshold.collect() === sameSummary.recallByThreshold.collect())
    assert(
      summary.precisionByThreshold.collect() === sameSummary.precisionByThreshold.collect())
  }

  test("evaluate with labels that are not doubles") {
    // Evaluate a test set with Label that is a numeric type other than Double
    val lr = new LogisticRegression()
      .setMaxIter(1)
      .setRegParam(1.0)
    val model = lr.fit(dataset)
    val summary = model.evaluate(dataset).asInstanceOf[BinaryLogisticRegressionSummary]

    val longLabelData = dataset.select(col(model.getLabelCol).cast(LongType),
      col(model.getFeaturesCol))
    val longSummary = model.evaluate(longLabelData).asInstanceOf[BinaryLogisticRegressionSummary]

    assert(summary.areaUnderROC ~== longSummary.areaUnderROC absTol 1E-10)
  }

  test("statistics on training data") {
    // Test that loss is monotonically decreasing.
    val lr = new LogisticRegression()
      .setMaxIter(10)
      .setRegParam(1.0)
      .setThreshold(0.6)
    val model = lr.fit(dataset)
    assert(
      model.summary
        .objectiveHistory
        .sliding(2)
        .forall(x => x(0) >= x(1)))

  }

  test("binary logistic regression with weighted samples") {
    val (dataset, weightedDataset) = {
      val nPoints = 1000
      val coefficients = Array(-0.57997, 0.912083, -0.371077, -0.819866, 2.688191)
      val xMean = Array(5.843, 3.057, 3.758, 1.199)
      val xVariance = Array(0.6856, 0.1899, 3.116, 0.581)
      val testData =
        generateMultinomialLogisticInput(coefficients, xMean, xVariance, true, nPoints, 42)

      // Let's over-sample the positive samples twice.
      val data1 = testData.flatMap { case labeledPoint: LabeledPoint =>
        if (labeledPoint.label == 1.0) {
          Iterator(labeledPoint, labeledPoint)
        } else {
          Iterator(labeledPoint)
        }
      }

      val rnd = new Random(8392)
      val data2 = testData.flatMap { case LabeledPoint(label: Double, features: Vector) =>
        if (rnd.nextGaussian() > 0.0) {
          if (label == 1.0) {
            Iterator(
              Instance(label, 1.2, features),
              Instance(label, 0.8, features),
              Instance(0.0, 0.0, features))
          } else {
            Iterator(
              Instance(label, 0.3, features),
              Instance(1.0, 0.0, features),
              Instance(label, 0.1, features),
              Instance(label, 0.6, features))
          }
        } else {
          if (label == 1.0) {
            Iterator(Instance(label, 2.0, features))
          } else {
            Iterator(Instance(label, 1.0, features))
          }
        }
      }

      (spark.createDataFrame(sc.parallelize(data1, 4)),
        spark.createDataFrame(sc.parallelize(data2, 4)))
    }

    val trainer1a = (new LogisticRegression).setFitIntercept(true)
      .setRegParam(0.0).setStandardization(true)
    val trainer1b = (new LogisticRegression).setFitIntercept(true).setWeightCol("weight")
      .setRegParam(0.0).setStandardization(true)
    val model1a0 = trainer1a.fit(dataset)
    val model1a1 = trainer1a.fit(weightedDataset)
    val model1b = trainer1b.fit(weightedDataset)
    assert(model1a0.coefficients !~= model1a1.coefficients absTol 1E-3)
    assert(model1a0.intercept !~= model1a1.intercept absTol 1E-3)
    assert(model1a0.coefficients ~== model1b.coefficients absTol 1E-3)
    assert(model1a0.intercept ~== model1b.intercept absTol 1E-3)
  }

  test("logistic regression with all labels the same") {
    val sameLabels = dataset
      .withColumn("zeroLabel", lit(0.0))
      .withColumn("oneLabel", lit(1.0))

    // fitIntercept=true
    val lrIntercept = new LogisticRegression()
      .setFitIntercept(true)
      .setMaxIter(3)

    val allZeroInterceptModel = lrIntercept
      .setLabelCol("zeroLabel")
      .fit(sameLabels)
    assert(allZeroInterceptModel.coefficients ~== Vectors.dense(0.0) absTol 1E-3)
    assert(allZeroInterceptModel.intercept === Double.NegativeInfinity)
    assert(allZeroInterceptModel.summary.totalIterations === 0)

    val allOneInterceptModel = lrIntercept
      .setLabelCol("oneLabel")
      .fit(sameLabels)
    assert(allOneInterceptModel.coefficients ~== Vectors.dense(0.0) absTol 1E-3)
    assert(allOneInterceptModel.intercept === Double.PositiveInfinity)
    assert(allOneInterceptModel.summary.totalIterations === 0)

    // fitIntercept=false
    val lrNoIntercept = new LogisticRegression()
      .setFitIntercept(false)
      .setMaxIter(3)

    val allZeroNoInterceptModel = lrNoIntercept
      .setLabelCol("zeroLabel")
      .fit(sameLabels)
    assert(allZeroNoInterceptModel.intercept === 0.0)
    assert(allZeroNoInterceptModel.summary.totalIterations > 0)

    val allOneNoInterceptModel = lrNoIntercept
      .setLabelCol("oneLabel")
      .fit(sameLabels)
    assert(allOneNoInterceptModel.intercept === 0.0)
    assert(allOneNoInterceptModel.summary.totalIterations > 0)
  }

  test("read/write") {
    def checkModelData(model: LogisticRegressionModel, model2: LogisticRegressionModel): Unit = {
      assert(model.intercept === model2.intercept)
      assert(model.coefficients.toArray === model2.coefficients.toArray)
      assert(model.numClasses === model2.numClasses)
      assert(model.numFeatures === model2.numFeatures)
    }
    val lr = new LogisticRegression()
    testEstimatorAndModelReadWrite(lr, dataset, LogisticRegressionSuite.allParamSettings,
      checkModelData)
  }

  test("should support all NumericType labels and not support other types") {
    val lr = new LogisticRegression().setMaxIter(1)
    MLTestingUtils.checkNumericTypes[LogisticRegressionModel, LogisticRegression](
      lr, spark) { (expected, actual) =>
        assert(expected.intercept === actual.intercept)
        assert(expected.coefficients.toArray === actual.coefficients.toArray)
      }
  }
}

object LogisticRegressionSuite {

  /**
   * Mapping from all Params to valid settings which differ from the defaults.
   * This is useful for tests which need to exercise all Params, such as save/load.
   * This excludes input columns to simplify some tests.
   */
  val allParamSettings: Map[String, Any] = ProbabilisticClassifierSuite.allParamSettings ++ Map(
    "probabilityCol" -> "myProbability",
    "thresholds" -> Array(0.4, 0.6),
    "regParam" -> 0.01,
    "elasticNetParam" -> 0.1,
    "maxIter" -> 2,  // intentionally small
    "fitIntercept" -> true,
    "tol" -> 0.8,
    "standardization" -> false,
    "threshold" -> 0.6
  )

  def generateLogisticInputAsList(
    offset: Double,
    scale: Double,
    nPoints: Int,
    seed: Int): java.util.List[LabeledPoint] = {
    generateLogisticInput(offset, scale, nPoints, seed).asJava
  }

  // Generate input of the form Y = logistic(offset + scale*X)
  def generateLogisticInput(
      offset: Double,
      scale: Double,
      nPoints: Int,
      seed: Int): Seq[LabeledPoint] = {
    val rnd = new Random(seed)
    val x1 = Array.fill[Double](nPoints)(rnd.nextGaussian())

    val y = (0 until nPoints).map { i =>
      val p = 1.0 / (1.0 + math.exp(-(offset + scale * x1(i))))
      if (rnd.nextDouble() < p) 1.0 else 0.0
    }

    val testData = (0 until nPoints).map(i => LabeledPoint(y(i), Vectors.dense(Array(x1(i)))))
    testData
  }

  /**
   * Generates `k` classes multinomial synthetic logistic input in `n` dimensional space given the
   * model weights and mean/variance of the features. The synthetic data will be drawn from
   * the probability distribution constructed by weights using the following formula.
   *
   * P(y = 0 | x) = 1 / norm
   * P(y = 1 | x) = exp(x * w_1) / norm
   * P(y = 2 | x) = exp(x * w_2) / norm
   * ...
   * P(y = k-1 | x) = exp(x * w_{k-1}) / norm
   * where norm = 1 + exp(x * w_1) + exp(x * w_2) + ... + exp(x * w_{k-1})
   *
   * @param weights matrix is flatten into a vector; as a result, the dimension of weights vector
   *                will be (k - 1) * (n + 1) if `addIntercept == true`, and
   *                if `addIntercept != true`, the dimension will be (k - 1) * n.
   * @param xMean the mean of the generated features. Lots of time, if the features are not properly
   *              standardized, the algorithm with poor implementation will have difficulty
   *              to converge.
   * @param xVariance the variance of the generated features.
   * @param addIntercept whether to add intercept.
   * @param nPoints the number of instance of generated data.
   * @param seed the seed for random generator. For consistent testing result, it will be fixed.
   */
  def generateMultinomialLogisticInput(
      weights: Array[Double],
      xMean: Array[Double],
      xVariance: Array[Double],
      addIntercept: Boolean,
      nPoints: Int,
      seed: Int): Seq[LabeledPoint] = {
    val rnd = new Random(seed)

    val xDim = xMean.length
    val xWithInterceptsDim = if (addIntercept) xDim + 1 else xDim
    val nClasses = weights.length / xWithInterceptsDim + 1

    val x = Array.fill[Vector](nPoints)(Vectors.dense(Array.fill[Double](xDim)(rnd.nextGaussian())))

    x.foreach { vector =>
      // This doesn't work if `vector` is a sparse vector.
      val vectorArray = vector.toArray
      var i = 0
      val len = vectorArray.length
      while (i < len) {
        vectorArray(i) = vectorArray(i) * math.sqrt(xVariance(i)) + xMean(i)
        i += 1
      }
    }

    val y = (0 until nPoints).map { idx =>
      val xArray = x(idx).toArray
      val margins = Array.ofDim[Double](nClasses)
      val probs = Array.ofDim[Double](nClasses)

      for (i <- 0 until nClasses - 1) {
        for (j <- 0 until xDim) margins(i + 1) += weights(i * xWithInterceptsDim + j) * xArray(j)
        if (addIntercept) margins(i + 1) += weights((i + 1) * xWithInterceptsDim - 1)
      }
      // Preventing the overflow when we compute the probability
      val maxMargin = margins.max
      if (maxMargin > 0) for (i <- 0 until nClasses) margins(i) -= maxMargin

      // Computing the probabilities for each class from the margins.
      val norm = {
        var temp = 0.0
        for (i <- 0 until nClasses) {
          probs(i) = math.exp(margins(i))
          temp += probs(i)
        }
        temp
      }
      for (i <- 0 until nClasses) probs(i) /= norm

      // Compute the cumulative probability so we can generate a random number and assign a label.
      for (i <- 1 until nClasses) probs(i) += probs(i - 1)
      val p = rnd.nextDouble()
      var y = 0
      breakable {
        for (i <- 0 until nClasses) {
          if (p < probs(i)) {
            y = i
            break
          }
        }
      }
      y
    }

    val testData = (0 until nPoints).map(i => LabeledPoint(y(i), x(i)))
    testData
  }
}

redpajama_set_name: RedPajamaGithub


Our Catalog is a wealth of information about WMHFA, class offerings, schedules, costs and many other valuable details every family needs to know concerning WMHFA.
Before contacting us with your question please look in the WMHFA catalog for the answer. Click on the button below to view our catalog.
Please contact us with any questions or comments you may have. We're happy to respond to any inquiries about WMHFA and our classes.


The Women's Leadership Unbound Summit is by invitation only. Below please find a summary of the registration fee categories as well as links to request an invitation. We hope that you will join us in Half Moon Bay!
GENERAL PARTNERS/FUND MANAGERS/FEE-EARNING LPs (Private Equity/Buyouts Firm, Private Credit Fund, Venture Capital Firm, Growth Equity Firm, Real Estate/Infrastructure/Real Assets Inv. Firm, Hedge Fund, Secondary Firm, Mezzanine Firm, Other Fund Manager, Lender, Investment Bank, Private Equity Fund of Funds, Hedge Fund of Funds, Multi-Family Office, LP Advisor, Outsourced CIO, Wealth Management).
If you are a non-fee-earning LP that does Direct Investing only (rather than Fund Investing) and you are from a Pension, Endowment, Foundation or Single-Family Office, you are eligible for a reduced Summit registration rate of $495.
Complimentary registrations are available for non-fee-earning Limited Partners actively investing in private equity and alternative investment funds. Approval for complimentary registrations for non-fee-earning LPs is based on the discretion of the conference organizers.
We also offer discounted registration rates for LPs whose organizations require payment of a conference registration fee and for ILPA members.
In order to maintain our target audience composition, a limited number of Service Providers are welcome to attend if their organization sponsors the Summit. Please contact Carolyn O'Donnell at Carolyn.ODonnell@falkmarquesgroup.com or +1.781.652.0900 to discuss the wide range of sponsorship opportunities.
To request an invitation to the Women's Leadership Unbound Summit, please use one of the links below.
Written cancellations received via email at Rebecca.Greenlee@falkmarquesgroup.com through Wednesday, July 10, 2019 will receive a refund according to the refund method requested: 1) if the refund is requested via check, registrant will receive a refund of the registration fee paid, less a $450 administrative fee, per person; or 2) if the refund is requested via credit card (to the card used to complete the registration), registrant will receive a refund of the registration fee paid, less a $450 administrative fee and a 5% credit card processing fee. After Wednesday, July 10, 2019, no refunds will be made for any reason. However, at any time if you cancel within 24 hours of registering, you will receive a refund, less a 10% processing fee, regardless of the refund method. Falk Marques Group is not responsible for cancellations that were never received. Please email Rebecca.Greenlee@falkmarquesgroup.com to confirm receipt of any such requests.
Requests to transfer a registration to another person within the same company must be made via email to Rebecca.Greenlee@falkmarquesgroup.com. Any transfer requests must be to an individual at an equivalent or higher level of seniority than the original registrant, and only one transfer request will be allowed per original registration. To ensure that we deliver a carefully balanced Summit audience, all transfer requests are subject to the approval of Falk Marques Group. Shared registrations are not permitted. Registrations may not be transferred to a future Summit. Payment for any of the optional networking activities is not refundable. Any travel or lodging costs are not the responsibility of the conference organizer and separate hotel payment and cancellation policies apply.
There will be no exceptions to these policies for any reason. Thank you for your understanding.


Dear Sir or Madam, our seminar for people with special needs and their faithful assistance dogs wouldn't take place without your support. Thank you for your aid. We not only have pleased four-legged friends, but also could recommend new products to weak-sighted people. We are also grateful for easy work with you. We hope that we will work together again next year.


text:
Febraf
Home Uncategorized How Alison Botha Survived A Brutal Attack By The 'ripper Rapists'
How Alison Botha Survived A Brutal Attack By The 'ripper Rapists'
There, they brutally raped her, disemboweled her, and slashed her throat so deeply that she was practically decapitated. After an ordinary evening out with her friends, Alison Botha drove back to her apartment in Port Elizabeth, South Africa. But as soon because the 27-year-old parked her automotive, a person with a knife pressured his method inside.
On 1 March 2013, Gauteng schoolgirl Keamogetswe Sefularo was allegedly fatally stabbed by one other lady from the same college. Although before dying Sefularo allegedly informed her mom her attacker stated they have been attacking her as a result of "she frolicked with Satanists", the incident was labelled "Satanic" in the media. On 5 March 2013, Gauteng MEC for Education Barbara Creecy announced a "gang" of ten other learners on the faculty, including two present on the incident, had been suspended from school pending investigations into the incident. Gauteng Department of Education spokesperson Charles Phahlane mentioned their suspension associated to alleged involvement in "dangerous spiritual practices", which Creecy stated department officials have been told might have led to the incident. South African Council of Churches chairperson Mautji Pataki commented "We are gravely involved that evil spirits are driving harmless youngsters to commit horrendous crimes." The Human Sciences Research Council statutory body released its report on what it describes as "the first complete evaluation of human trafficking in South Africa" in March 2010.
Alison's knife-wielding attacker pounced after she'd parked her car at her apartment. On December 18, 1994, Alison Botha was abducted near her house in South Africa. By the top of the night time, she had been raped, stabbed, and disemboweled — but she was nonetheless alive.
His death sentence was commuted to life imprisonment in 1993. In 1997 he said the lacking girls have been killed throughout Satanic rituals and have been the victims of worldwide baby pornography rings, however no evidence of this was ever found. In 2001 he was sentenced to an additional six years' imprisonment for perjury for false statements he made concerning the missing women. In July 2008 Johannes Petrus Lotter and his spouse Maria Magdalena Lotter had been murdered in their home in the Westville suburb of Durban. Their daughter Nicolette, their son Hardus and Nicolette's boyfriend Mathew Naidoo have been arrested shortly afterwards.
But when she pulled up and was preparing to take the brief stroll into her house, a man with a knife stormed her vehicle. I left my then-girlfriend at residence and took my finest buddy . "I wanted to make it clear that they had been a menace to society and will never be released," Judge Chris Jansen said in the movie.
The tall, attractive former head woman was abducted outdoors her Port Elizabeth home by Satanists Frans du Toit and Theuns Kruger. A literal belief in Satan is remarkably prevalent right here even after opponents of apartheid proved not to be the anti-Christ. Sights such as a satanic witch having stigmata down her arms in the form of a pentagram and triple sixes, and watching a boy being exorcised, feeling the demons kicking in opposition define traunch to his stomach, confirmed Jonker's rebirth in Christ. The colonial time period witchcraft utilized by Petrus in a conventional African context has robust adverse connotations and relates to a perception in dangerous magic.
He said children youthful than 18 aren't welcome in the church and Satanic rituals are misinterpreted by many people claiming to be Satanists. An article by Khuthala Nandipha in the Mail & Guardian newspaper highlighted group ignorance about Satanism and sure contributory elements including social decay and inequality and the abuse of alcohol and drugs such as nyaope. On 31 October 2005, Charles Jacobs was killed in a Paarl LDS church where he worked as custodian. The scene was staged as a crucifixion and the word "Satun" was written on the floor in blood. Although police spokespersons mentioned it was most likely a "botched housebreaking" not an occult homicide, the killing was described as "Satanic" and "occult-style" in the media.
When she completed her education, she spent a couple of years traveling. And after she returned home, Botha found a job as an insurance dealer, which she enjoyed. When Alison Botha was attacked in 1994, she was stabbed 30 instances and practically decapitated. Theuns kruger 'Alison Botha rapists may make parole bid from at present' By Giovanna Gerbi Frans du Toit and Theuns Kruger are serving life sentences for raping and stabbing her 21 years in the past. Friends and family known as Humpel an outstanding detective, an avid canine lover and a person who gave again to the group.
Alison Botha was having a standard day along with her friends when an opportunity encounter turned her life the different means up. All it took was parking in a dimly lit area to draw an individual who claimed to be possessed by a demon. Former police brigadier turned ward councillor John Best described Humpel as an excellent detective who had gone out of his means to assist folks. Humpel was the lead investigator in several sensational cases together with the homicide of rugby administrator Merwe Swart, gunned down in his driveway in Port Elizabeth in 1996, in addition to the robbery-murder of SABC cameraman Eddie Ellis in 1997.
Du Toit and Kruger each acquired life sentences in August 1995. Satanism was not thought of a mitigating issue in their sentencing. Jonker left the police pressure after suffering a coronary heart attack in 2000 and subsequently practised as a pastoral counsellor. He has been a director of the Act-Up Support Christian prayer ministry since 2002 and has worked as a consultant for the SAPS Detective Academy in Hammanskraal since his retirement. Other former members of the unit became concerned in Christian deliverance ministry and therapy, together with former Eastern Cape coordinator of the unit James Lottering and former Free State coordinator of the unit Johan de Beer . De Beer founded Auksano in 1998 while working for SAPS and resigned from SAPS in 2002.
Febraf is a blog all about gaming, technology, and lifestyle. The blog posts are based on the author's personal experiences with games or tech as well as industry news. Febraf covers topics such as video games, gadgets, geek culture and more.
© 2022 FEBRAF - All Rights Reserved.

redpajama_set_name: RedPajamaCommonCrawl


text:
Man 'caught trying to import £10 million of cocaine into the UK hidden in a van carrying frozen fish'
Cookham man 'caught trying to import £10 million of cocaine into the UK hidden in a van carrying frozen fish'
By Stephanie Wareham @Stephanie_BFP Audience and Content Editor
A Cookham man was allegedly caught trying to import £10 million of cocaine into the UK - hidden in a van carrying frozen fish.
James Satterley was reportedly stopped by Border Force officers as he drove off the ferry at Newhaven Port in East Sussex, early on Saturday morning.
The 50-year-old of Cookham, was said to be transporting around 100 kilograms of cocaine wrapped in 97 one-kilo packages hidden in a load of frozen fish.
National Crime Agency (NCA) officers seized the haul and questioned Satterley before charging him with importing class A drugs.
The drugs have now been taken away for further analysis.
An NCA spokesperson said: "If cut and sold at street level it is likely the haul would have had a potential value of around £10 million."
NCA Senior Investigating Officer Paul Morris added: "NCA officers are working alongside Border Force, Sussex Police, Thames Valley Police and the South East Regional Organised Crime Unit to investigate this seizure and the circumstances around it.
"Working with partners we are determined to do all we can to disrupt the organised crime groups involved in the large-scale importation of class A drugs, which fuel serious violence and impact some of the most vulnerable members of our society. Making seizures like this is an important part of that work."
Satterley is being held in custody and will appear before Brighton magistrates today (November 25)
School celebrates 'outstanding' Ofsted
Best yoga classes in Bucks - As voted for by you
Ambulance service responded to more than 20,000 999 calls during Christmas and New Year's Day
PICTURE: Our favourite Camera Club photo of the week
Enjoy idyllic Snowdrop Sunday stroll for hospice
Hilarious committee comedy comes to the stage
OPINION: Why you should volunteer for the NSPCC
LETTERS: 'Former town councillors over the centuries must be turning in their graves'
FOOD REVIEW: Beaconsfield has a new runner on its foodie-focused scene


Turn Your Driveway Into A Beautifully Inviting Entryway to Your Home.
Getting a new driveway design and installation isn't your everyday or even once-a-year chores. It's a serious project that takes a lot of time, consideration, and expertise to decide when's the right time and what's the best way to do it. We at J.F.D. Landscapes understand that a new driveway installation is a major undertaking – and we're here to help you along every step of the way.
Being in the landscape design business for over 25 years now, we've installed hundreds of driveways. And the experience we have gained through all of those projects is passed onto your driveway installation project when you choose to work with us.
First, our landscape design team will meet with you to create a design and choose a material that fits your home's style, your personal taste, and your budget. We work with stone driveways that can add natural beauty to your home. We work with brick driveways which adds a rustic touch to your landscape design. And, as an Authorized Unilock Contractor, we work with Unilock brick pavers that enhance flexibility and customizability in both design and driveway installation. Each material has its own benefits and considerations, and our teams will spend as much time as it takes to help you choose the perfect material for your driveway.
Then, we'll survey your property, as the topography of your land can have a major impact on how we design and install your driveway. Some companies will sell you on a design that's not as functional as it is beautiful, which will only leave you with a structurally unsound and not-so-durable driveway.
Once you're happy with our strategic and functional driveway design plan, we start digging. Throughout your driveway installation, you'll find that our teams of technicians and craftsmen treat you with unparalleled professionalism. And that they leave your property neat and tidy after every day on the job.
When it's all said and done, you'll have a beautiful driveway with no complaints and no need to take on this job again for decades to come.
Our crews are made up of true craftsmen, not just skilled workers. Because we believe it takes more than technique to create the perfect landscape design. That's why we only hire the most professional and passionate candidates to join our team.
We primarily work in the areas of Moreland Hills, Auburn Center, Novelty, Gates Mills, Twinsburg, Shaker Heights, Beachwood, Mayfield Heights, Solon, Aurora, & Hudson. But we're always open to new projects and opportunities in neighboring areas.
We've been in the landscaping and lawn care industry for decades. We started out working in the field as laborers. Now, as owners of a landscape contractor business, we truly understand what it takes to get the job done right.
I know there are a lot of good landscape companies and it's hard to choose. But no one is going to give you bad references or show you poor photos of their work. J.F.D. Landscapes, Inc. offers you one thing that no one else can, me, Joe Drake. I am President of J.F.D. Landscapes, Inc. and no one will be more focused on making sure you get the quality and value you paid for, I guarantee it.
Thank you for the opportunity to become your landscape contractor. I appreciate it.


Hey Michael, are you still around?
i pop in now and then. Been designing movie poster for the past few years. working on some life size art soon.
I am such a huge fan. I haven't seen much from you in awhile. You still working your magic?
Woow i want to be a digitale artisti like u!
But draw is difficult for me!


text:
Uncover the stories behind one..
History and wine come together under the roof of a...
Chocolate heaven: Belgian choc..
History and wine come together under the roof of a new five-star hotel from the 18th century
By: Alexandra Stilwell
Palace of wine: The new palacete in Lisbon, built by an architect ahead of his time, "offers discerning travellers a sophisticated wine experience in the heart of the Portuguese capital"
At the crossroads of Lisbon's Chiado, Bairro Alto and Príncipe Real neighbourhoods, a new five-star hotel blends some of the city's architectural foundations with the country's most remarkable wine traditions.
The extravagant Palácio Ludovice Wine Experience Hotel is set within an 18th-century palacete named after its founder, Johann Friedrich Ludwig. The German-born goldsmith and architect, who changed his name to Ludovice when he worked in Rome, moved to Portugal to become King D. João V's architect in the early 1700s.
In search of a place to build a private residence, he found the perfect spot in the very heart of the city. Overlooking the Miradouro de São Pedro de Alcântara, his large palacete, the first to occupy an entire block, was one of the few buildings to have survived the Great Lisbon Earthquake in 1755. This was thanks to the architect's innovative construction technique, which the Marquês de Pombal later used as inspiration to rebuild the city.
Three centuries later, another architect has taken on the responsibility of perpetuating the building's heritage, bringing its rooms and halls back to life as 61 contemporary rooms and suites. Acclaimed architect and designer Miguel Câncio Martins – who designed the iconic Buddha Bar in Paris in the 1990s and, more recently, the sumptuous Quinta da Comporta Hotel on the Portuguese coast – brought the palacete into the 21st century. Today, it offers discerning travellers a sophisticated wine experience in the heart of the Portuguese capital.
Under his care, the original white-and-blue 18th-century tiles, fresco paintings and stucco ceilings were restored, and a new and palatial accommodation arose, marking the beginning of a new era in this historical landmark.
Behind its bold yellow façade, the hotel's lobby gives guests a good idea of what they are about to experience. The tall ceiling covered in metallic vine-shaped lamp fixtures, an extra-large portrait of Ludovice against a bare brick wall, and an irregularly shaped green carpet evoking the terraced vineyards of the Douro Valley create a glamorous interior where wine is celebrated above all.
To the left of the entrance, the Solar do Vinho do Porto (Port Wine Institute), which took up residence on the ground floor of the palacete in 1946, gives visitors a glimpse of delicious Douro and Port vintages.
Throughout the hotel, a labyrinth of corridors is lined with vineyard-themed carpets, and large stone staircases are flanked with blue and white tiles. Golden retro details combined with original fresco murals give it the perfect "Old World meets New World" look, and a chapel with Masonic symbols and a Hebraic inscription adds an air of mystery.
Each exquisite room and suite of the Palácio Ludovice boasts a unique décor. Carefully designed to bring back elements of the 18th century with a contemporary feel, they offer the most luxurious modern comforts.
Overlooking the vertical garden within the inner courtyard or offering a bird's eye view over the city, the rooms feature elegant bespoke furniture, original hand-painted tiles and carved wood on the ceilings blended with modern design, materials and colourful fabrics.
The grand luminous bathrooms with gold-trimmed mirrors, large walk-in showers and stand-alone baths almost feel like private spas.
Speaking of which, on the ground floor, the hotel features Lisbon's only Caudalie Vinothérapie Boutique Spa, where guests can enjoy body treatments and facials in two sumptuous rooms.
Sitting under the magnificent skylight of a five-story atrium, Federico, the hotel's superb restaurant and bar, offers in-depth wine-tasting experiences in an incredible setting.
With a penchant for dramatic interiors, Câncio Martins enhanced the existing architectural features of the building to create a striking indoor patio where lisboetas and visitors alike come to see and be seen.
Filled with bright natural light during the day, at night, Federico's courtyard turns into an intimate setting for a gourmet dinner or evening drink at the bar.
Vine-shaped lighting fixtures lead the way into the airy atrium, where a lush vertical garden brings life to an otherwise sober interior. A constellation of lamps dangles above the marble tables from the glass ceiling, mimicking Lisbon's starry sky on a clear night. And surrounding the patio, the large ancient stone arches, where 'Federico' is written in neon lights, and a fountain have been refurbished, revealing the building's authenticity.
The bistro-style tables, with wicker chairs and long plush benches strewn with cushions, populate the restaurant floor. Earthy tones of beige, brown and green create a space of warmth and understated sophistication.
Along the kitchen wall, large shelves covered in decorative pieces and books add a slight disarray to the scene.
Here, chef Ricardo Simões marvels foodies with a mix of traditional Portuguese and French comfort food, innovatively and daringly interpreted, and paired with an enviable selection of wines.
The menu kickstarts with a traditional Portuguese chicken soup (canja de galinha), followed by a light and creamy burrata with tomato and basil. Classic French-inspired dishes include a pumpkin and chèvre cheese soufflé, a perfectly executed steak tartare and foie gras terrine with red onion jam, whilst more exotic suggestions include a monkfish carpaccio with passion fruit and lime and green eggs with guacamole.
For the main course, the Tornedó steak with foie gras and mushrooms is the epitome of French flavours. Portuguese cuisine is evoked in the typical Coimbra-style chanfana (goat stew), accompanied by truffled mashed potatoes and sprout foam, a cod confit with chickpea, with coriander sauce and pork feet, and monkfish rice with shrimp. Vegetarian dishes are no less aromatic, with the spicy vegetable curry, savoury linguini al pesto and unctuous mushroom risotto with pumpkin.
Delighting his diners with his ingenuity, for dessert, chef Simões serves the most unexpected reinterpretation of the Pastel de Nata with coffee ice cream and caramel sauce, among other delights.
Staying true to Palácio Ludovice's oenological identity, the restaurant has compiled a list of Portuguese wines worthy of a knowledgeable palate. Paying homage to the country's wine-making history, Federico's selection proudly represents each Portuguese wine-producing region, including the island of Madeira and the Algarve.
At the back of the restaurant, Federico's pièce de resistance, a vibrant signature bar built on the foundations of a former cellar, exudes a cosmopolitan atmosphere. From late morning to midnight, it is the place to sip cocktails and rare vintages poured by the glass, paired with succulent snacks.
Lisbon's hospitality scene may be in full effervescence with new hotels offering five-star accommodation, but few boast as much history, heritage and style as the Palácio Ludovice.
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First Art Expo Algarve happens this February in Portimão


text:
Michael Jeffery est un homme d'affaires britannique né le  à Peckham, dans le sud de Londres, et mort le  dans la collision aérienne de Nantes.

Il est principalement connu pour avoir été le manager des Animals et de Jimi Hendrix dans les années 1960. Après la mort de Hendrix, en 1970, il supervise la publication des premiers albums posthumes du guitariste. Ce rôle est repris après lui par le producteur Alan Douglas.

Liens externes

Naissance en mars 1933
Naissance à Londres
Agent artistique
Décès en mars 1973
Décès à 39 ans
Mort dans un accident aérien
Décès dans la Loire-Atlantique


Robert Dixon was walking along Mill Road, Cobholm, on his way into town when he heard a loud crash followed by a cloud of brick dust.
The 32-year-old initially thought it was a digger releasing its load until a wall behind it collapsed.
He made a split decision to investigate further and saw a man covered in blood and pinned by a steel pipe and a pile of bricks in half-sitting position.
Fearing the man could be seriously hurt he told him not to move and carefully removed the bricks on top without moving the ones that could have caused further slippage, before calling for an ambulance.
Later, Mr Dixon checked on the condition of the injured worker with workers at the site who said he was back at home and doing well.
The ten apartments being built on the site are taking shape under the government's help to buy scheme and will be completed by the second half of next year.
The golf day was organised by Pink Office, who specialise in office products and services, and was attended by over 120 customers, suppliers and friends of the company.
Participants were treated to refreshments, prizes and a fantastic course with all entry fees on the day being combined with a charity auction to smash the company's target for the day.
The proceeds raised from the day on September 1 have gone to Breast Cancer Care, Cancer Research UK and Sarcoma UK.
The show was a triumphant return for Andre after fans were left disappointed by the cancellation of a recent show in Great Yarmouth.
Andre's Managing Agent, Marie Claire of Can Associates, issued a statement at the time saying, "The star was devastated that 'severe sickness' forced him to cancel his Great Yarmouth show on 1st September 2018".
However, last Sunday, the popular singer arrived at Potters on time with over 700 fans trying desperately to get a glimpse of him, some of which were offered a discount price to match the cancelled Britannia Pier events.
The fans also got their autographs and hugs from their idol who is celebrating his 25th year in show business.
The interactive table at Hickling House displays numerous images and games onto a table where it stimulates residents to join in and take part in numerous activities.
Great Yarmouth Borough Council is holding its final public exhibition event on Friday, September 21 in Hemsby as part of the initial public consultation to shape a key planning document to help guide and enable sustainable growth, development, jobs and investment until 2030 and beyond.
The council's draft Local Plan Part Two includes provisional policies for deciding planning applications, suggested allocations of land for development, and also a proposed reduction in the overall housing target. The final public exhibition is taking place at Hemsby Sports & Social Club, Waters Lane, from 1pm to 7pm.
Those wishing to comment as part of the consultation can do it online at www.great-yarmouth.gov.uk/planning-consultations or by completing a Local Plan comments form. All comments must be received before midnight on Sunday, September 30.
Comments forms are available from the Town Hall and all public libraries in the borough.


No news around. We have had plenty this week with the announcement of the Clooney Foundation setting up schools in Lebanon for refugees. Really? isn't there refugees living in tent cities with little food, water or shelter? Education is important just wondering why schools in Lebanon when there are a lot of refugee children desperate just for a home.
For the announcement Amal spent over 3,000 dollars on a "dress" cause you know she can't just wear anything not while she has George Clooneys money to spend. There are a few stories about Amal and her sometimes very ugly but expensive outfits. Over $70,000 of oufits in one week. Like seriously and they can't help get food and shelter for refugees not in Lebanon. Guess we know who has the balls in the family and it isn't George.
It was big news when Angelina Jolie and Brad Pitt split after 12 years together and they were really in love. George and Amal look anything but like a loved up couple let's hope that ends soon, It is coming across more as a PR and Business relationship and many people have noticed and are calling them out on it.


The Onyx 9 oz Tumbler Cup is a stainless steel cup with a smooth rolled lip.
Perfect size for little hands, and no fear of broken glass when dropped!
Great cups. My 3 year old son loves them. Very easy for him to hold and use without spilling. We will be ordering more soon.
Awesome product. Great non-plastic cups for kids or adults. And we'll have them forever.
Love it! Perfeft for little hands, smooth and light weight. My son likes how shiny it is. I highly recommend this cup.
I really love these, the quality is good and they are perfect cups for children. No plastic, no breakage!
Perfect size for the kids or myself. Perfect alternative to plastic especially for kids. You don't have to worry about them breaking it. Very smooth and shiny. Highly recommended!
Great size for all the family, very smooth material, clean up nice and shiny.
Metal is smooth, washes well in dishwasher. Great size for kids and self. Much preferred over plastic alternatives.
Love these tumblers. Just the right size for my toddler's hands, and we use the Sip-snap silicone tops with these. We are avoiding plastic, these fit the bill. They come out of the dishwasher looking great. I'm so thankful for the Well.ca email notifications when out-of-stock items are back in stock, because I waited for these are I'm very happy with them.


Have to agree with this, ha, going back about 19 years, son was able to drive it home and third gear seems about right.
I ended up doing a complete rebuild myself with the lesson series from the guy at transmission bench .
Runs perfectly and came in just about $400 with all new stuff including the torque converter.
The job is not to hard but it took me a long time because I did it in my spare time and who has that? Not me.
Glad to hear it, and well done.
Hello, I completed the rebuild I used the ATSG 4l60e book and vids from trans bench.
works great but now I discovered a left axle leak so I'm fixing that.


Global Tech giant IBM is taking online identity authentication seriously.
The company has teamed-up with SecureKey Technologies to work together to enable a new digital identity and attribute sharing network based on IBM Blockchain.
The network will be designed to make it easier for consumers to verify they are who they say they are, in a privacy-enhanced, security-rich and efficient way. When launched later this year, consumers can use the network to instantly verify their identity for services such as new bank accounts, driver's licenses or utilities.
The network is currently in the testing phase in Canada, and once it goes live later in 2017 Canadian consumers will be able to opt-in to the new blockchain-based service using a mobile app.
Consumers – or network members – will be able to control what identifying information they share from trusted credentials to the organizations of their choice, for those organizations to quickly and efficiently validate the consumer's identity and arrange new services.
For example, if a consumer has proven their identity with their bank and a credit agency, they can grant permission to share their data with a utility to create a new account. Since the bank and the credit agency have already gone through extensive verification of the consumer's identity, the utility can choose to rely on the fact that the information is verified, and the consumer can be approved for new services.
"What IBM is building with SecureKey and members of the digital identity ecosystem in Canada, including major banks, telecom companies and government agencies, will help tackle the toughest challenges surrounding identity," said Marie Wieck, general manager, IBM Blockchain.
Canada's leading banks, including BMO, CIBC, Desjardins, RBC, Scotiabank and TD joined the digital identity ecosystem in October, 2016, investing $27 million collectively in SecureKey.


If you are young, passionate founder of operating startup in ICT, web, mobile or security, do not miss the opportunity to participate in the third edition of Startup Tel Aviv Bootcamp. The international contest organized by the Minister of Foreign Affairs of Israel, Tel Aviv city and Google Israel, gets together the best startuppers coming from 20 countries.
The winner of the contest will represent Italy in Israel on the bootcamp from 14 to 19 September 2014.
Following the usual period of hype, as it happens for the majority of tech trends, 2014 seems to be a transitional year for crowdfunding. A year of further growth and consolidation, of course. Several platforms have been created but only a few of them have been able to cut through the noise, actually.
Three models have basically emerged: the reward-based approach and the equity-based one. A third model, hybrid between them, seeing the presence of thematic platforms (in areas such as real estate, music, education, research) is rising.
Now all the guests of LUISS ENLABS can also experience the first Bitcoin ATM in Italy.
Yesterday, Robocoin CEO, Jordan Kelley, has personally inaugurated the first Robocoin Kiosk in frames of the 4th edition of No Cash Day promoted by the Italian Parliament member Sergio Boccadutri and organized by CashlessWay. Headlined as "Bitcoin in the real economy", this event included the public hearing at the Chamber of Deputies with the participation of industry leaders from Hyperion, Unicredit, the Italian Banking Association, Mastercard, the Bitcoin Foundation and others.
We proudly announce Roberto Ascione, President of Razorfish Healthware, just joined our Board of Advisors this week!
We asked Ascione a few questions to get to know him better!
Today, Federica Guidi, Minister of Economic Development, was at LUISS ENLABSto announce Italia Startup Visa, a program to incentivize innovative entrepreneurs from all over the world to start up their business in Italy!
Italia Startup Visa means leaner and faster procedures to facilitate the entry visa approval for autonomous work for extra-EU citizens who want to establish an innovative startup in Italy. Any extra-EU citizen who has a good business idea, whatever age or background can apply.


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Actor Kevin Spacey faces more sexual assault charges in Britain
16th November 2022 16th November 2022 1 Min Read
LONDON, Nov 16 (Reuters) – Britain's Crown Prosecution Service (CPS) has authorised seven additional charges against Oscar-winning actor Kevin Spacey for a number of sexual assaults against one man between 2001 and 2004, it said on Wednesday.
The new CPS charges include one of causing a person to engage in sexual activity without consent and others of indecent and sexual assault. The new charges follow a review of evidence gathered by British police.
Spacey, 63, was previously accused of five offences in Britain – four counts of sexual assault by touching, and a more serious charge of causing a person to engage in penetrative sexual activity without consent.
His lawyer had said in June Spacey had "strenuously" denied those allegations of sex offences.
Spacey's defence lawyer in Britain, Patrick Gibbs, did not immediately respond to an email seeking comment on the latest charges.
In the United States, Spacey last month defeated a sexual abuse case against him after jurors in a Manhattan civil trial found his accuser did not prove his claim that Spacey made an unwanted sexual advance on him when he was 14.
(Reporting by Farouq Suleiman and Sachin Ravikumar; Editing by Kate Holton and Toby Chopra)
Once you're here...
Sanremo Song Festival gets off to flying start
Cde8th February 2023
Comedy series Fawlty Towers to return after more than 40 years.
Disney's Hong Kong service drops 'Simpsons' episode with 'forced labor' reference
Photo Story: The 73rd Sanremo Music Festival
Beyonce breaks all-time Grammy wins record, Harry Styles claims best album
Actor Mark Hamill to sell signed Star Wars posters to raise cash for Ukraine
Ozzy Osbourne 'not physically capable' of upcoming tour
Beyond beep beep: Video game music goes to next level at Grammys
Cde3rd February 2023
Opera devoted to Raffaella Carrà to debut in September in Italy
Cde2nd February 2023


This archive diagram is for some old specially wired Original Quad coil pickups with the exposed copper back.
Archive wiring diagram for special Original Quad coil pickups wired with 6-conductor cable.
This shows how to connect a 4 conductor pickup to a Music Man PCB.
The MME and I4E pickups have 3 coils for hum cancelling when coil switched. The MME was discontinued in 2007.
The E92 was built between 1992 and 2004.
The E90 distortion humbucker was built from 1990-2000.
The E88-D could drive a V88-C for a full active setup. The E88-D was discontinued in 1997.
The E88-D Active Humbucker was build between 1988 and 1997.
Pickups with names ending in KE are generally split coil active pickups and can be wired in different ways.
The 9E was an active J-Bass pickup built from 1992-2008.
The 8TC could be paired with a 9V stacked coil J-Bass pickup for a full-active setup.
The 8TC was an active P-Bass pickup we made from 1993 to 2007.
The 8E was an active P-Bass pickup discontinued in the mid 1980s.
The 6JH Mute Compartment pickup for Rickenbacker basses was built from 2001 to 2012.
The 3S Tapped Single Coil pickup was discontinued in 2012.
The Beast was discontinued in the early '90s.