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package de.jungblut.classification.tree;

import com.google.common.base.Preconditions;
import com.google.common.collect.Lists;
import de.jungblut.classification.AbstractClassifier;
import de.jungblut.datastructure.ArrayUtils;
import de.jungblut.math.DoubleVector;
import de.jungblut.math.dense.DenseDoubleVector;
import de.jungblut.math.sparse.SparseDoubleVector;
import de.jungblut.math.tuple.Tuple;
import gnu.trove.iterator.TDoubleIterator;
import gnu.trove.iterator.TIntObjectIterator;
import gnu.trove.map.hash.TIntIntHashMap;
import gnu.trove.map.hash.TIntObjectHashMap;
import gnu.trove.set.hash.TDoubleHashSet;
import gnu.trove.set.hash.TIntHashSet;
import org.apache.commons.math3.util.FastMath;
import org.apache.hadoop.io.WritableUtils;

import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
import java.util.Arrays;
import java.util.Iterator;
import java.util.List;
import java.util.Random;

/**
 * A decision tree that can be used for classification with numerical or
 * categorical features. The tree is built by maximizing information gain using
 * the ID3 algorithm. If no featureTypes were supplied, the default is assumed
 * to be nominal features at all feature dimensions. <br/>
 * Instances can be created by the static factory methods #create().
 *
 * @author thomasjungblut
 */
public final class DecisionTree extends AbstractClassifier {

    private static final double LOG2 = FastMath.log(2);

    private AbstractTreeNode rootNode;
    private FeatureType[] featureTypes;
    private int numRandomFeaturesToChoose;
    private int maxHeight = 25;
    private long seed = System.currentTimeMillis();

    // default is binary classification 0 or 1.
    private boolean binaryClassification = true;
    private int outcomeDimension;
    private int numFeatures;

    // use the static factory methods!
    private DecisionTree() {
    }

    // serialization constructor
    private DecisionTree(AbstractTreeNode rootNode, FeatureType[] featureTypes,
                         boolean binaryClassification, int numFeatures, int outcomeDimension) {
        this.binaryClassification = binaryClassification;
        this.rootNode = rootNode;
        this.featureTypes = featureTypes;
        this.numFeatures = numFeatures;
        this.outcomeDimension = outcomeDimension;
    }

    @Override
    public void train(DoubleVector[] features, DoubleVector[] outcome) {
        Preconditions.checkArgument(features.length == outcome.length,
                "Number of examples and outcomes must match!");
        // assume all nominal if nothing was set
        if (featureTypes == null) {
            featureTypes = new FeatureType[features[0].getDimension()];
            Arrays.fill(featureTypes, FeatureType.NOMINAL);
        }
        Preconditions.checkArgument(
                featureTypes.length == features[0].getDimension(),
                "FeatureType length must match the dimension of the features!");
        binaryClassification = outcome[0].getDimension() == 1;
        if (binaryClassification) {
            outcomeDimension = 2;
        } else {
            outcomeDimension = outcome[0].getDimension();
        }
        numFeatures = features[0].getDimension();
        TIntHashSet possibleFeatureIndices = getPossibleFeatures();
        // recursively build the tree...
        rootNode = build(Lists.newArrayList(features), Lists.newArrayList(outcome),
                possibleFeatureIndices, 0);
    }

    @Override
    public DoubleVector predict(DoubleVector features) {
        int clz = rootNode.predict(features);
        if (clz < 0) {
            // let's assume the default case ("negative") here, instead of making NPEs
            // in other areas, as the callers aren't nullsafe
            clz = 0;
        }
        if (binaryClassification) {
            return new DenseDoubleVector(new double[]{clz});
        } else {
            DoubleVector vec = outcomeDimension > 10 ? new SparseDoubleVector(
                    outcomeDimension) : new DenseDoubleVector(outcomeDimension);
            vec.set(clz, 1);
            return vec;
        }
    }

    /**
     * @return a random subset of the features.
     */
    TIntHashSet chooseRandomFeatures(TIntHashSet possibleFeatureIndices) {
        if (numRandomFeaturesToChoose > 0
                && numRandomFeaturesToChoose < numFeatures
                && possibleFeatureIndices.size() > numRandomFeaturesToChoose) {
            // make a copy
            TIntHashSet set = new TIntHashSet();
            int[] arr = possibleFeatureIndices.toArray();
            Random rnd = new Random(seed);
            while (set.size() < numRandomFeaturesToChoose) {
                set.add(arr[rnd.nextInt(arr.length)]);
            }
            return set;
        }
        return possibleFeatureIndices;
    }

    /**
     * Recursively build the decision tree in a top down fashion.
     */
    private AbstractTreeNode build(List<DoubleVector> features,
                                   List<DoubleVector> outcome, TIntHashSet possibleFeatureIndices, int level) {

        // we select a subset of features at every tree level
        possibleFeatureIndices = chooseRandomFeatures(possibleFeatureIndices);

        int[] countOutcomeClasses = getPossibleClasses(outcome);
        TIntHashSet notZeroClasses = new TIntHashSet();
        for (int i = 0; i < countOutcomeClasses.length; i++) {
            if (countOutcomeClasses[i] != 0) {
                notZeroClasses.add(i);
            }
        }

        // if we only have a single class to predict, we will create a leaf to
        // predict it
        if (notZeroClasses.size() == 1) {
            return new LeafNode(notZeroClasses.iterator().next());
        }

        // if we don't have anymore features to split on or reached the max height,
        // we will use the majority class and predict it
        if (possibleFeatureIndices.isEmpty() || level >= maxHeight) {
            return new LeafNode(ArrayUtils.maxIndex(countOutcomeClasses));
        }

        // now we can evaluate the infogain for every possible feature and choose
        // that one that maximizes it and split on it
        double targetEntropy = getEntropy(countOutcomeClasses);
        Split[] infoGain = new Split[numFeatures];
        for (int featureIndex : possibleFeatureIndices.toArray()) {
            infoGain[featureIndex] = computeSplit(targetEntropy, featureIndex,
                    countOutcomeClasses, features, outcome);
        }

        // pick the split with highest info gain
        int maxIndex = 0;
        double maxGain = infoGain[maxIndex] != null ? infoGain[maxIndex]
                .getInformationGain() : Integer.MIN_VALUE;
        for (int i = 1; i < infoGain.length; i++) {
            if (infoGain[i] != null && infoGain[i].getInformationGain() > maxGain) {
                maxGain = infoGain[i].getInformationGain();
                maxIndex = i;
            }
        }
        Split bestSplit = infoGain[maxIndex];
        int bestSplitIndex = bestSplit.getSplitAttributeIndex();
        if (featureTypes[bestSplitIndex].isNominal()) {
            TIntHashSet uniqueFeatures = getNominalValues(bestSplitIndex, features);
            NominalNode node = new NominalNode(bestSplitIndex, uniqueFeatures.size());
            int cIndex = 0;
            for (int nominalValue : uniqueFeatures.toArray()) {
                node.nominalSplitValues[cIndex] = nominalValue;
                Tuple<List<DoubleVector>, List<DoubleVector>> filtered = filterNominal(
                        features, outcome, bestSplitIndex, nominalValue);
                TIntHashSet newPossibleFeatures = new TIntHashSet(
                        possibleFeatureIndices);
                // remove that feature
                newPossibleFeatures.remove(bestSplitIndex);
                node.children[cIndex] = build(filtered.getFirst(),
                        filtered.getSecond(), newPossibleFeatures, level + 1);
                cIndex++;
            }
            // make a faster lookup by sorting
            node.sortInternal();
            return node;
        } else {
            // numerical split
            TIntHashSet newPossibleFeatures = new TIntHashSet(possibleFeatureIndices);
            Tuple<List<DoubleVector>, List<DoubleVector>> filterNumeric = filterNumeric(
                    features, outcome, bestSplitIndex,
                    bestSplit.getNumericalSplitValue(), true);
            Tuple<List<DoubleVector>, List<DoubleVector>> filterNumericHigher = filterNumeric(
                    features, outcome, bestSplitIndex,
                    bestSplit.getNumericalSplitValue(), false);

            if (filterNumeric.getFirst().isEmpty()
                    || filterNumericHigher.getFirst().isEmpty()) {
                newPossibleFeatures.remove(bestSplitIndex);
            } else {
                // we changed something, thus we can unselect all numerical features
                for (int i = 0; i < featureTypes.length; i++) {
                    if (featureTypes[i].isNumerical()) {
                        newPossibleFeatures.add(i);
                    }
                }
            }

            // build subtrees
            AbstractTreeNode lower = build(filterNumeric.getFirst(),
                    filterNumeric.getSecond(), new TIntHashSet(newPossibleFeatures),
                    level + 1);
            AbstractTreeNode higher = build(filterNumericHigher.getFirst(),
                    filterNumericHigher.getSecond(),
                    new TIntHashSet(newPossibleFeatures), level + 1);
            // now we can return this completed node
            return new NumericalNode(bestSplitIndex,
                    bestSplit.getNumericalSplitValue(), lower, higher);
        }

    }

    /**
     * Filters all examples where the feature at the given index has NOT the
     * specific value. So the returned lists contain only vectors where the
     * feature has the specific value.
     *
     * @return a new tuple of two new lists (features and their outcome).
     */
    private Tuple<List<DoubleVector>, List<DoubleVector>> filterNominal(
            List<DoubleVector> features, List<DoubleVector> outcome,
            int bestSplitIndex, int nominalValue) {

        List<DoubleVector> newFeatures = Lists.newArrayList();
        List<DoubleVector> newOutcomes = Lists.newArrayList();

        Iterator<DoubleVector> featureIterator = features.iterator();
        Iterator<DoubleVector> outcomeIterator = outcome.iterator();
        while (featureIterator.hasNext()) {
            DoubleVector feature = featureIterator.next();
            DoubleVector out = outcomeIterator.next();
            if (((int) feature.get(bestSplitIndex)) == nominalValue) {
                newFeatures.add(feature);
                newOutcomes.add(out);
            }
        }

        return new Tuple<>(newFeatures, newOutcomes);
    }

    /**
     * Filters the lists by numerical decision.
     *
     * @param features       the features to filter.
     * @param outcome        the outcome to filter.
     * @param bestSplitIndex the feature split index with highest information
     *                       gain.
     * @param splitValue     the value of the split point.
     * @param lower          true if the returned list should contain lower items, else it
     *                       contains strictly higher items.
     * @return two filtered parallel lists.
     */
    private Tuple<List<DoubleVector>, List<DoubleVector>> filterNumeric(
            List<DoubleVector> features, List<DoubleVector> outcome,
            int bestSplitIndex, double splitValue, boolean lower) {

        List<DoubleVector> newFeatures = Lists.newArrayList();
        List<DoubleVector> newOutcomes = Lists.newArrayList();

        Iterator<DoubleVector> featureIterator = features.iterator();
        Iterator<DoubleVector> outcomeIterator = outcome.iterator();
        while (featureIterator.hasNext()) {
            DoubleVector feature = featureIterator.next();
            DoubleVector out = outcomeIterator.next();
            if (lower) {
                if (feature.get(bestSplitIndex) <= splitValue) {
                    newFeatures.add(feature);
                    newOutcomes.add(out);
                }
            } else {
                if (feature.get(bestSplitIndex) > splitValue) {
                    newFeatures.add(feature);
                    newOutcomes.add(out);
                }
            }

        }

        return new Tuple<>(newFeatures, newOutcomes);
    }

    /**
     * Computes the split of nominal and numerical values.
     *
     * @param overallEntropy      the overall entropy at the given time.
     * @param featureIndex        the feature index to evaluate on.
     * @param countOutcomeClasses the histogram over all possible outcome classes.
     * @param features            the features.
     * @param outcome             the outcome.
     * @return a {@link Split} that contains a possible split (either numerical or
     * categorical) along with the information gain.
     */
    private Split computeSplit(double overallEntropy, int featureIndex,
                               int[] countOutcomeClasses, List<DoubleVector> features,
                               List<DoubleVector> outcome) {

        if (featureTypes[featureIndex].isNominal()) {
            TIntObjectHashMap<int[]> featureValueOutcomeCount = new TIntObjectHashMap<>();
            TIntIntHashMap rowSums = new TIntIntHashMap();
            int numFeatures = 0;
            Iterator<DoubleVector> featureIterator = features.iterator();
            Iterator<DoubleVector> outcomeIterator = outcome.iterator();
            while (featureIterator.hasNext()) {
                DoubleVector feature = featureIterator.next();
                DoubleVector out = outcomeIterator.next();
                int classIndex = getOutcomeClassIndex(out);
                int nominalFeatureValue = (int) feature.get(featureIndex);
                int[] is = featureValueOutcomeCount.get(nominalFeatureValue);
                if (is == null) {
                    is = new int[outcomeDimension];
                    featureValueOutcomeCount.put(nominalFeatureValue, is);
                }
                is[classIndex]++;
                rowSums.put(nominalFeatureValue, rowSums.get(nominalFeatureValue) + 1);
                numFeatures++;
            }
            double entropySum = 0d;
            // now we can calculate the entropy
            TIntObjectIterator<int[]> iterator = featureValueOutcomeCount.iterator();
            while (iterator.hasNext()) {
                iterator.advance();
                int[] outcomeCounts = iterator.value();
                double condEntropy = rowSums.get(iterator.key()) / (double) numFeatures
                        * getEntropy(outcomeCounts);
                entropySum += condEntropy;
            }
            return new Split(featureIndex, overallEntropy - entropySum);
        } else {
            // numerical case
            Iterator<DoubleVector> featureIterator = features.iterator();
            TDoubleHashSet possibleFeatureValues = new TDoubleHashSet();
            while (featureIterator.hasNext()) {
                DoubleVector feature = featureIterator.next();
                possibleFeatureValues.add(feature.get(featureIndex));
            }
            double bestInfogain = -1;
            double bestSplit = 0.0;
            TDoubleIterator iterator = possibleFeatureValues.iterator();
            while (iterator.hasNext()) {
                double value = iterator.next();
                double ig = computeNumericalInfogain(features, outcome, overallEntropy,
                        featureIndex, value);
                if (ig > bestInfogain) {
                    bestInfogain = ig;
                    bestSplit = value;
                }
            }
            return new Split(featureIndex, bestInfogain, bestSplit);
        }

    }

    /**
     * This method computes the numerical information gain for the given features
     * and outcomes and a featureIndex and its value. This is done by iterating
     * once over all features and outcomes and calculating a table of outcome
     * counts given a higher/lower relationship to the given feature value.
     *
     * @param features       the features.
     * @param outcome        the outcomes.
     * @param overallEntropy the overall entropy of the selectable features.
     * @param featureIndex   the feature index to check.
     * @param value          the value that acts as a possible split point between a lower
     *                       and higher partition for a given feature.
     * @return the information gain under the feature given a value as a split
     * point.
     */
    private double computeNumericalInfogain(List<DoubleVector> features,
                                            List<DoubleVector> outcome, double overallEntropy, int featureIndex,
                                            double value) {
        double invDatasize = 1d / features.size();
        // 0 denotes lower than or equal, 1 denotes higher
        int[][] counts = new int[2][outcomeDimension];
        int lowCount = 0;
        int highCount = 0;
        Arrays.fill(counts, new int[outcomeDimension]);
        Iterator<DoubleVector> featureIterator = features.iterator();
        Iterator<DoubleVector> outcomeIterator = outcome.iterator();
        while (featureIterator.hasNext()) {
            DoubleVector feature = featureIterator.next();
            DoubleVector out = outcomeIterator.next();
            int idx = getOutcomeClassIndex(out);
            if (feature.get(featureIndex) > value) {
                counts[1][idx]++;
                highCount++;
            } else {
                counts[0][idx]++;
                lowCount++;
            }
        }

        // discount the lower set
        overallEntropy -= (lowCount * invDatasize * getEntropy(counts[0]));
        // and the higher one
        overallEntropy -= (highCount * invDatasize * getEntropy(counts[1]));
        return overallEntropy;
    }

    /**
     * @return the class index, this takes binary classification into account as
     * well as multi class classification.
     */
    private int getOutcomeClassIndex(DoubleVector out) {
        int classIndex = 0;
        if (binaryClassification) {
            classIndex = (int) out.get(0);
        } else {
            classIndex = out.maxIndex();
        }
        return classIndex;
    }

    /**
     * @return a set of nominal values of that feature index given the examples
     * that contain this feature.
     */
    private TIntHashSet getNominalValues(int featureIndex,
                                         List<DoubleVector> features) {
        TIntHashSet uniqueFeatures = new TIntHashSet();
        for (DoubleVector vec : features) {
            int featureValue = (int) vec.get(featureIndex);
            uniqueFeatures.add(featureValue);
        }
        return uniqueFeatures;
    }

    /**
     * @return an array from 0-outcome dimension that has a count on every feature
     * index representing how often it occurred.
     */
    private int[] getPossibleClasses(List<DoubleVector> outcome) {
        int[] clzs = new int[outcomeDimension];
        for (DoubleVector out : outcome) {
            if (binaryClassification) {
                clzs[(int) out.get(0)]++;
            } else {
                clzs[out.maxIndex()]++;
            }
        }

        return clzs;
    }

    /**
     * Sets the type of feature per index. This should match the inputted number
     * of features in the training method. If this isn't set at all, all
     * attributes are assumed to be nominal.
     *
     * @return this decision tree instance.
     */
    public DecisionTree setFeatureTypes(FeatureType[] featureTypes) {
        this.featureTypes = featureTypes;
        return this;
    }

    /**
     * Sets the number of random features to choose from all features.Zero,
     * negative numbers or numbers greater than the really available features
     * indicate all features to be used.
     *
     * @return this decision tree instance.
     */
    public DecisionTree setNumRandomFeaturesToChoose(int numRandomFeaturesToChoose) {
        this.numRandomFeaturesToChoose = numRandomFeaturesToChoose;
        return this;
    }

    /**
     * Sets the maximum height of this tree.
     *
     * @return this instance.
     */
    public DecisionTree setMaxHeight(int max) {
        this.maxHeight = max;
        return this;
    }

    /**
     * Sets the seed for a random number generator if used.
     */
    public DecisionTree setSeed(long seed) {
        this.seed = seed;
        return this;
    }

    /*
     * for testing
     */
    void setNumFeatures(int numFeatures) {
        this.numFeatures = numFeatures;
    }

    /**
     * @return the set of possible features
     */
    TIntHashSet getPossibleFeatures() {
        // all features are possible here
        TIntHashSet possibleFeatureIndices = new TIntHashSet();
        for (int i = 0; i < numFeatures; i++) {
            possibleFeatureIndices.add(i);
        }
        return possibleFeatureIndices;
    }

    /**
     * Writes the given tree to the output stream. Note that the stream isn't
     * closed here.
     */
    public static void serialize(DecisionTree tree, DataOutput out)
            throws IOException {
        try {
            out.writeBoolean(tree.binaryClassification);
            WritableUtils.writeVInt(out, tree.outcomeDimension);
            WritableUtils.writeVInt(out, tree.numFeatures);
            for (int i = 0; i < tree.featureTypes.length; i++) {
                WritableUtils.writeVInt(out, tree.featureTypes[i].ordinal());
            }
            out.writeBoolean(false);
            tree.rootNode.write(out);
        } catch (Exception e) {
            throw new IOException(e);
        }
    }

    /**
     * Reads a new tree from the given stream. Note that the stream isn't closed
     * here.
     */
    public static DecisionTree deserialize(DataInput in) throws IOException {
        boolean binary = in.readBoolean();
        int outcomeDimension = WritableUtils.readVInt(in);
        int numFeatures = WritableUtils.readVInt(in);
        FeatureType[] arr = new FeatureType[numFeatures];
        for (int i = 0; i < numFeatures; i++) {
            arr[i] = FeatureType.values()[WritableUtils.readVInt(in)];
        }
        if (in.readBoolean()) {
            throw new IllegalArgumentException("unable to deserialize a compiled tree with this library version.");
        } else {
            AbstractTreeNode root = AbstractTreeNode.read(in);
            return new DecisionTree(root, arr, binary, numFeatures, outcomeDimension);
        }
    }

    /**
     * @return a default decision tree with all features beeing nominal.
     */
    public static DecisionTree create() {
        return new DecisionTree();
    }

    /**
     * Creates a new decision tree with the given feature types.
     *
     * @param featureTypes the types of the feature that must match the number of
     *                     features in length.
     * @return a default decision tree with all features beeing set to what has
     * been configured in the given array.
     */
    public static DecisionTree create(FeatureType[] featureTypes) {
        return new DecisionTree().setFeatureTypes(featureTypes);
    }

    /**
     * @return the entropy of the given prediction class counts.
     */
    static double getEntropy(int[] outcomeCounter) {
        double entropySum = 0d;
        double sum = 0d;
        for (int x : outcomeCounter) {
            sum += x;
        }
        for (int x : outcomeCounter) {
            if (x == 0) {
                return 0d;
            }
            double conditionalProbability = x / sum;
            entropySum -= (conditionalProbability * log2(conditionalProbability));
        }

        return entropySum;
    }

    /**
     * @return the log2 of the given input.
     */
    private static double log2(double num) {
        return FastMath.log(num) / LOG2;
    }

}


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The American Alpine Club selected Denver as the first-ever North American tour stop for UIAA World Cup Ice Climbing Finals; Civic Center Park will serve as the destination for the 2019 event. The world's top men and women ice climbers will compete for glory on a 50-foot tall wall of sheer ice. Additionally, all weekend long, the park will be transformed into BARBEGAZI, a free winter festival with fun for all ages, including axe throwing, a huge ice maze, fat tire biking and a snowball target range. Spectators can also enjoy food trucks, fire pits, an ice bar, a beer garden and music.
U.S. Women's National Team vs. Australia (April 4)
U.S. Soccer's Women's National Team will take on Australia at Dick's Sporting Goods Park for a soccer match before they head to France to participate in the FIFA Women's World Cup. The match is part of the "Countdown to the Cup," a ten-game, ten-city tour running from January to May leading up to the tournament.
Monster Energy Supercross (April 13)
Experience the most competitive and highest-profile off-road motorcycle racing championship in the world. Monster Energy AMA Supercross, a FIM World Championship, is comprised of 17 heart-stopping races in North America's premier venues – including a stop at Broncos Stadium at Mile High. Every week, the greatest super cross racers from across the globe seek to outperform each other on the sport's most challenging tracks as they accumulate points leading up to the world championship. Featuring tight corners, fast straightaways, challenging rhythm sections, triple jumps and whoops sections, these tracks pack excitement into every inch of dirt.
National Collegiate Volleyball Federation Collegiate Club Championships (April 18-20)
Denver and the Colorado Convention Center will be the host city and venue for the 2019 NCVF Collegiate Club Championships. Nearly 450 teams are expected to enter the competition, which Denver will be hosting for the first time.
NCAA Division II Softball Championship (May 23-27)
Metropolitan State University (MSU) of Denver will host the national 2019 NCAA DII Softball Championship. MSU Denver previously hosted the 2016 NCAA Championships Festival in conjunction with the Denver Sports Commission, which featured softball, among several other collegiate sports.
North American Gay Volleyball Championships XXXVII (May 23-26)
Denver will host the North American Gay Volleyball Association (NAGVA) Championships XXVII at the Colorado Convention Center. Committed to promoting and developing the sport of volleyball in the LGBTQ community, NAGVA hosts these championships in a different city every year. Denver has previously been honored to host championships in 2000, then in 2007 for NAGVA's Silver Anniversary.
CONCACAF Gold Cup (June 19)
In mid-2018, the Confederation of North, Central America and Caribbean Association Football (CONCACAF) announced that Broncos Stadium at Mile High will be one of the 15 venues in 13 U.S. metropolitan cities that will welcome the 2019 edition of the CONCACAF Gold Cup. Taking place every two years, the CONCACAF Gold Cup features some of the Western Hemisphere's best players. It is the official national team championship of the region, crowning the best national team in North and Central America and the Caribbean. CONCACAF has announced that Mexico will be one of the four teams playing in this summer's double-header; and the other teams and the remainder of the schedule will be announced in coming months.
Colorado Classic (August 22-25)
New this year, race organizers announced that the 2019 Colorado Classic – previously a series of men's and women's races in three Colorado cities – will be a women's only race. It will be the only UCI standalone women's stage race in the Western hemisphere; raising the bar with a quadrupled prize purse, team stipends, live streaming and longer, more challenging routes.
NCAA Division II Women's Volleyball Championships (December 12-14)
Metropolitan State University (MSU) of Denver will host the national 2019 NCAA DII Women's Volleyball Championship. MSU Denver previously hosted the 2016 NCAA Championships Festival in conjunction with the Denver Sports Commission, which featured several collegiate sports.
VISIT DENVER Public Relations staff
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Boston Scientific has announced it's releasing the POLARIS Imaging System, a device used with the company's intravascular ultrasound (IVUS) catheters. It's designed for use with the company's existing coronary, peripheral, and intra-cardiac catheters, as well as upcoming fractional flow reserve (FFR) wire and new IVUS catheters that Boston Sci expects to release soon.
The company aimed to make the POLARIS more intuitive to use than previous generation IVUS systems, helping shorten procedure times, while assisting with image interpretation. The system has received both FDA clearance and CE Mark approval and has already been used in a number of clinics in the US. and Europe.
Boston Scientific will offer customers an opportunity to upgrade their existing iLab System to the state-of-the-art POLARIS System.


text:
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Movies — June 22, 2019
Posted 6/22/19
Capitol Theatre: "The Brain That Wouldn't Die" (G) 9. Cinema Capitol: "The Biggest Little Farm" (PG) 4, 7. "Porco Rosso" (PG) 4:15, 7:15. Rome Cinemas: "Child's Play" (R) 11, …
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Posted Saturday, June 22, 2019 4:00 pm
Capitol Theatre: "The Brain That Wouldn't Die" (G) 9.
Cinema Capitol: "The Biggest Little Farm" (PG) 4, 7. "Porco Rosso" (PG) 4:15, 7:15.
Rome Cinemas: "Child's Play" (R) 11, 1:15, 3:30, 5:45, 8, 10:20. "Toy Story 4" (G)11:10, 12:25, 1:35, 2:50, 4, 5:15, 6:30, 7:40, 8:55, 10:05. "Aladdin" (PG) 1, 3:50, 6:45, 9:35. "Godzilla: King of the Monsters" (PG-13) 4:15, 7:10. "Men in Black: International" (PG-13) 10:55, 1:45, 4:25, 7:20, 10. "Rocketman" (R) 11:15, 2, 4:45, 7:30, 10:15. "Secret Life of Pets 2" (PG) 11:30, 12:15, 2:25, 4:35, 7, 9:10. "Shaft" (R) 1:40, 10:10.
Marquee, New Hartford: "Anna" (R) 12:40, 3:40, 6:40, 9:25. "Child's Play" (R) 12:10, 2:40, 5, 7, 7:20, 9:20, 9:45. "Toy Story 4" MXC (G) noon, 12:30, 3, 3:30, 6, 6:30, 8:30, 9. "Toy Story 4" (G) 11:30, 1, 2:30, 5:30, 7. "Toy Story 4" 3D (G) 4, 9:30. "Men in Black: International" (PG-13) 12:50, 3:50, 6:50, 9:35. "Shaft" (R) 12:15, 3:15, 5:55. "Dark Phoenix" (PG-13) 12:40, 3:40. "Late Night" (R) 9. "Secret Life of Pets 2" (PG) 11:50, 2:10, 4:25, 6:40, 8:55. "Godzilla: King of Monsters" (PG-13) 12:20, 3:20, 6:20, 9:20. "Rocketman" (R) 1:10, 4:10, 7:05, 9:50. "Aladdin" (PG) 11:55, 2:50, 5:45, 8:40. "John Wick: Chapter 3 - Parabellum" (R) 12:45, 3:45, 6:45, 9:45. "Avengers" (PG-13) 8:35.
Glenwood, Oneida: "Anna" (R) 11:40, 2:20, 5, 7:45, 10:25. "Child's Play" (R) 11, 1:15, 3:30, 5:45, 8, 10:15. "Toy Story 4" (G) 11:10, 11:45, 1:35, 2:10, 4, 4:35, 6:30, 7, 8:55, 9:25. "Aladdin" (PG) 12:40, 3:30, 6:45, 9:35. "Late Night" (R) 11:55, 5:10, 7:35. "Men in Black: International" (PG-13) 10:55, 1:45, 4:25, 7:15, 9:55. "Rocketman" (R) 11:05, 1:55, 4:40, 7:25, 10:10. "Secret Life of Pets 2" (PG) 11:30, 1:40, 3:50, 6:15, 8:30. "Shaft" (R) 2:30, 10.
Stevens is Officer of the Month
Patrolman Eric A. Stevens has been named the Rome Police Department's Officer of the Month for June. Officials said Stevens responded to a motor vehicle accident at 4:30 a.m. June 7 and found the …
Zebb's in New Hartford closing, cites revenue, increased competition
NEW HARTFORD — Local business Zebb's Deluxe Bar & Grill, 8428 Seneca Turnpike, is closing. In a …
1 million tons and counting
Since its opening in 1991, the Oneida-Herkimer Solid Waste Authority's Recycling Center has processed …
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text:
namespace net {

// Used for reading SPDY frames. Though there isn't really anything terribly
// SPDY-specific here, it's a helper class that's useful when doing SPDY
// framing.
//
// To use, simply construct a SpdyFramerReader using the underlying buffer that
// you'd like to read fields from, then call one of the Read*() methods to
// actually do some reading.
//
// This class keeps an internal iterator to keep track of what's already been
// read and each successive Read*() call automatically increments said iterator
// on success. On failure, internal state of the SpdyFrameReader should not be
// trusted and it is up to the caller to throw away the failed instance and
// handle the error as appropriate. None of the Read*() methods should ever be
// called after failure, as they will also fail immediately.
class NET_EXPORT_PRIVATE SpdyFrameReader {
 public:
  // Caller must provide an underlying buffer to work on.
  SpdyFrameReader(const char* data, const size_t len);

  // Empty destructor.
  ~SpdyFrameReader() {}

  // Reads an 8-bit unsigned integer into the given output parameter.
  // Forwards the internal iterater on success.
  // Returns true on success, false otherwise.
  bool ReadUInt8(uint8* result);

  // Reads a 16-bit unsigned integer into the given output parameter.
  // Forwards the internal iterater on success.
  // Returns true on success, false otherwise.
  bool ReadUInt16(uint16* result);

  // Reads a 32-bit unsigned integer into the given output parameter.
  // Forwards the internal iterater on success.
  // Returns true on success, false otherwise.
  bool ReadUInt32(uint32* result);

  // Reads a 31-bit unsigned integer into the given output parameter. This is
  // equivalent to ReadUInt32() above except that the highest-order bit is
  // discarded.
  // Forwards the internal iterater (by 4B) on success.
  // Returns true on success, false otherwise.
  bool ReadUInt31(uint32* result);

  // Reads a 24-bit unsigned integer into the given output parameter.
  // Forwards the internal iterater (by 3B) on success.
  // Returns true on success, false otherwise.
  bool ReadUInt24(uint32* result);

  // Reads a string prefixed with 16-bit length into the given output parameter.
  //
  // NOTE: Does not copy but rather references strings in the underlying buffer.
  // This should be kept in mind when handling memory management!
  //
  // Forwards the internal iterater on success.
  // Returns true on success, false otherwise.
  bool ReadStringPiece16(base::StringPiece* result);

  // Reads a string prefixed with 32-bit length into the given output parameter.
  //
  // NOTE: Does not copy but rather references strings in the underlying buffer.
  // This should be kept in mind when handling memory management!
  //
  // Forwards the internal iterater on success.
  // Returns true on success, false otherwise.
  bool ReadStringPiece32(base::StringPiece* result);

  // Reads a given number of bytes into the given buffer. The buffer
  // must be of adequate size.
  // Forwards the internal iterater on success.
  // Returns true on success, false otherwise.
  bool ReadBytes(void* result, size_t size);

  // Seeks a given number of bytes into the buffer from the current offset.
  // Equivelant to an empty read.
  // Forwards the internal iterator.
  // Returns true on success, false otherwise.
  bool Seek(size_t size);

  // Rewinds this reader to the beginning of the frame.
  void Rewind() { ofs_ = 0; }

  // Returns true if the entirety of the underlying buffer has been read via
  // Read*() calls.
  bool IsDoneReading() const;

  // Returns the number of bytes that have been consumed by the reader so far.
  size_t GetBytesConsumed() const { return ofs_; }

 private:
  // Returns true if the underlying buffer has enough room to read the given
  // amount of bytes.
  bool CanRead(size_t bytes) const;

  // To be called when a read fails for any reason.
  void OnFailure();

  // The data buffer that we're reading from.
  const char* data_;

  // The length of the data buffer that we're reading from.
  const size_t len_;

  // The location of the next read from our data buffer.
  size_t ofs_;
};

}  // namespace net

#endif  // NET_SPDY_SPDY_FRAME_READER_H_

redpajama_set_name: RedPajamaGithub


text:
using System.Threading;
using System.Threading.Tasks;

namespace Titanium.Web.Proxy.StreamExtended.Network
{
    /// <summary>
    ///     A concrete implementation of this interface is required when calling CopyStream.
    /// </summary>
    public interface IHttpStreamWriter
    {
        bool IsNetworkStream { get; }

        void Write(byte[] buffer, int offset, int count);

        Task WriteAsync(byte[] buffer, int offset, int count, CancellationToken cancellationToken);

        ValueTask WriteLineAsync(CancellationToken cancellationToken = default);

        ValueTask WriteLineAsync(string value, CancellationToken cancellationToken = default);
    }
}


text:
Torrington's Ona Albaugh Signs with Chadron St. for Track
Frank Gambino
Frank Gambino Published: August 12, 2020
Torrington Telegram Torrington H.S.
Ona Albaugh of Torrington has elected to continue her track career at Chadron State in Nebraska. She qualified for the 3A State Track Meet three times and placed 9th in the 100-meter dash and 6th in the 200 in her junior year. As a sophomore, she placed 10th in the 100 and 12th in the 200 at the state meet and had a 13th place finish in the 3A 200 as a freshman.
Albaugh's senior season in outdoor track was canceled due to the pandemic but she did run indoor track and earned a spot in the state championship meet, competing for Cheyenne Central. She placed 27th in the 55-meter dash with a time of 7.87 and took 20th in the 200 meters with a clocking of 28.53.
Ona Albaugh-Torrington
Torrington Telegram
Torrington H.S.
Filed Under: Chadron State College, Ona Albaugh, Torrington Trailblazers, Track and Field
Categories: Articles, Torrington, Track & Field


text:
The U13 Coca-Cola Craven Week is about enjoyment as well as identifying talent
July 1, 2014 southcapenetGarden Route News
Durban, 1 July, 2014.
The 2014 Under 13 Coca-Cola Craven Week is under way at Glenwood Boys' High School in Durban and everyone here is in agreement that you would go a long way to find a tournament that has gotten off to better start.
The local organizers have been helped, of course, by the balmy East coast weather – which is being lapped up by the inland teams and their supporters, and Glenwood with its stands, pavilions and lush green field is the perfect venue for a week like this.
The Sharks Union pulled off a major coup by getting their Super Rugby squad involved. They spent the opening day at Glenwood – and they were all there, Springboks included. In a great move, the players joined the provinces that they were affiliated to as schoolboys for the march-on at the opening ceremony, and the team held their practice session on the field at the end of the day's play.
The Sharks leading the boys onto the field
The under-13 Coca-Cola Craven Week is all about rewarding players at the end of their primary school rugby careers, and about motivating them to continue their development when they go to high school next year. The participation of the Sharks on the opening day certainly went a long way towards achieving those goals.
Yusuf Jackson, SA Rugby's manager of amateur rugby, who is at the week in Durban, stresses the fact that this particular week is about giving the current top primary schools players an opportunity to compete on a national stage.
"We are aware that these players are at the start of their rugby careers. The under-13 Coca-Cola Craven Week features the early developers and we don't pay too much attention to results at this stage," he said.
Cobus Reinach at Day 1
"So, this week is all about participation, and about making the provinces aware of the young talent they have in their ranks. There's no point in trying to identify future stars at this stage because there are many players who are not here who will develop physically in the years to come."
At the same time, however, it's important that no talented players should fall through the cracks and there are enough agents and marketing people from the top rugby schools around the country to ensure that that doesn't happen.
Fetsi Mbele, Coca-Cola South Africa Marketing Assets Manager tells us that the goals of the tournament also extend further than what happens on the field, "Not only does the Under-13 Coca-Cola Craven Week unearth talented youngsters it also teaches them to live a healthy and active life through their sport of choice."
For the spectators, in the meantime, it's been a feast of barefoot rugby, on a lush emerald field, in Durban's sub-tropical winter sunshine – it doesn't get much better than that.
Day 2 Results at lunch
Leopards 32 – 14 Griquas
Blue Bulls 17 – 19 SWD
Free State 12 – 24 Pumas
Namibia 5 – 12 Griffons
Zimbabwe 31 – 27 Border CD
About The Coca-Cola Company
The Coca-Cola Company(NYSE: KO) is the world's largest beverage company, refreshing consumers with more than 500 sparkling and still brands. Led by Coca-Cola, the world's most valuable brand, the Company's portfolio features 15 billion dollar brands including Diet Coke, Fanta, Sprite, Coca-Cola Zero, vitaminwater, Powerade, Minute Maid, Simply and Georgia. Globally, we are the No. 1 provider of sparkling beverages, juices and juice drinks and ready-to-drink teas and coffees. Through the world's largest beverage distribution system, consumers in more than 200 countries enjoy the Company's beverages at a rate of 1.7 billion servings a day. With an enduring commitment to building sustainable communities, our Company is focused on initiatives that reduce our environmental footprint, support active, healthy living, create a safe, inclusive work environment for our associates, and enhance the economic development of the communities where we operate. For more information about our Company, please visit our website at www.thecoca-colacompany.com.
rugby, schools, sport
SWD's top umpire included in CSA Reserve Panel
CSA announces dates for national youth tournaments
2012 Christmas Treasure Box/Bag Project
SWD Cricket mourns passing of Japtha Julius
SWD Cricket announces new appointments & promotions
Minister Mitchell visits Garden Route District
St Francis Bay's property market bounces back
Magnificent Western Province leads six unbeaten teams into the third round of the U13 Coca-Cola Craven Week


1982 Porsche 911SC For your consideration is a Euro Spec 911 finished in Indishrot (India Red) over chocolate leather. This is a very well maintained and excellent running Porsche. -Exterior- The body is in good condition. There is absolutely no rust top or bottom. -Interior- The interior is in overall good condition. The seats show a minimal amount of wear. but are great for being 33 years old. The carpets. door panels dash are all in good condition. The targa top is also in good condition inside and out. -Engine & Transmission- This 911 has been owned by a Porsche mechanic who kept the car is top mechanical condition…the car runs. drives shifts and brakes perfectly. This Porsche wears brand new BFG g-force tires all around. I have 3 sets of keys as well as all of the original books. This 911 is free on any liens. I have a clean title in hand and it is ready for a new home. -Terms of Sale- I have full faith in the quality of this vehicle and confidence in the accuracy of it's description. unfortunately disclaimers have become part of life these days. hence the following: Buyers are more than welcome. and even encouraged to inspect the vehicle in person BEFORE the auction ends. Do all of your homework and due diligence before the auction ends and bid accordingly!!!! Please do not assume anything and DO ask questions you might have in regard to this car. Please remember that this is a vehicle that is 33 years old. so even though it is in outstanding physical and mechanical condition in and out expect some normal wear and tear. This vehicle is being sold AS-IS. where is. with no warranty expressed. written. or implied. The seller shall not be responsible for the correct description. authenticity. genuineness. or defects herein. and makes no warranty in connection therewith. No allowance or set aside will be made on account of any incorrectness. imperfection. defect or damage. Any descriptions or representations are for identification purposes only and are not to be construed as a warranty of any type. It is the responsibility of the buyer to have thoroughly inspected the vehicle. asked any and as much questions they might have about the vehicle. and to have satisfied himself or herself as to the condition and value and to bid based upon that judgment solely. Seller assumes no responsibility for any repairs regardless of any oral or written statements about the vehicle. $500 non-refundable deposit is due via PayPal within 24 hours after the end of the auction. and winning bidder must contact me within 72 hours to arrange payment and shipping details. The balance (certified bank/cashier's check. wire transfer or cash. in person) is due within 7 days after the end of the auction!! No Exceptions!!! All financial transactions must be completed before vehicle delivery. Please have all of your finances ready to meet the terms of sale! If you need any special arrangements in regard to the payment. please let me know in a timely manner. I am easy to work with and accommodate any reasonable requests. If terms of sale are not met (deposit within 24 hours. and full payment within 7 days). nbsp;that bidder will be reported to e-bay. and the car will be re-listed!! -Shipping- This vehicle must be picked up within TWO weeks of auction end unless special arrangements are made in advance. I encourage both DOMESTIC and INTERNATIONAL bidders to bid on this car. and I will gladly accommodate the winning bidder with his/her shipping arrangements. I routinely ship to Rotterdam and Bremerhaven and will happily arrange shipping with a reliable and inexpensive transport company. I can also accommodate requests to have the car sipped to any port in the world! Shipping arrangements and costs are the responsibility of the winning bidder! Good luck and happy bidding!!!
1984 Porsche 911 Carrera Cabriolet - Euro Spec,86K Orig Miles,Fuchs, & Records!
2.0L 160 HP Flat-6 "Boxer"


Sayyora Muin (Muinova), born 1977 in Tashkent / Uzbekistan, lives & works since 2003 in Berlin. Her artistic path began with an education as a costume designer and followed by studies in book illustration in Tashkent. She continued her studies at the UdK Berlin in 2003 and was in 2010/2011 a master student of Prof. Florence von Gerkan for stage costumes. In addition to several exhibitions in Germany and Uzbekistan she also worked as a costume designer in many film and theater projects in both countries. Her great passion as a costume designer is reflected in her art. Both her drawings as well as her photographs and object art are shaped by this passion.
Zebo Nasirova, born 1953 in Dushanbe / Tajikistan, lives and works for decades in Tashkent. After studying painting in Tajikistan, she went to Uzbekistan to study stage design at the University of Art and Theatre. She was also in the master class at the All-Russian State University of Cinematography (VGIK) in Moscow. Zebo Nasirova is one of the most renowned costume designers of Uzbekistan, who has participated in numerous international flm productions. Since 1979 she is participating in national and international exhibitions such as in Russia, Uzbekistan and Algeria. In Germany her pictures can be seen for the first time.
Islam Hasanalizade, born 1988 in Baku, graduated from the Azerbaijan State Academy of Art. He is one of the new young artists of Azerbaijan. Being the winner of the competition "America through my eyes" in Baku indicates a promising future. He had already quite many exhibitions, mostly in Azerbaijan, but also in Georgia and Russia. He also exhibites for the first time in Germany.
The artist Sayyora Muin is present at the opening.


text:
Niclas Leif Rasck (born 10 March 1969) is a Swedish former footballer who played as a defender and spent most of his career with Djurgårdens IF. He made 152 appearances for Djurgården.

Prior to joining the Stockholm club, he played for Örebro SK and Ludvika FK, where his career began. He declared his retirement from professional football at the end of the 2006 season.

Honours
Djurgårdens IF
 Allsvenskan: 2002, 2003, 2005
 Superettan: 2000
 Svenska Cupen: 2002, 2004, 2005

References

1969 births
Living people
Association football defenders
Swedish footballers
Allsvenskan players
Superettan players
Djurgårdens IF Fotboll players
Örebro SK players

redpajama_set_name: RedPajamaWikipedia


text:
The Rule Against Perpetuities, The Fertile Octogenarian, and The Most Boring Luncheon Speech
We set through a painfully boring luncheon presentation the other day. It was on quantum physics, string theory, and who knows what else. We didn't pay for the lunch, but we sure earned it.
We decided to come up with the most boring subject for a presentation, one that would even supplant quantum physics. The possibilities included:
The Practical Application of Presentism and Becoming
The Practical Application of Isometrics in an Isolated Isosceles Triangle
A Discourse on Reason and Revelation in the Dark Ages – A True Oxymoron
A Look at Diffusion in an Opaque World
The Econometrics of Regression Analysis in the 15th Century
Metaphysics, Phenomenology and Edmund Husserl
A Theoretical Mass Spectrometry of an Amoeba
The Calculus of a Flea Circus
The Bifurcation of a Dichotomy in Distributed Processes
An Empirical Study of Contested Tender Offers During the Decade of the
1960's (my actual doctoral dissertation)
It was a tough call, but none of these qualified.
The clear winner was "The Definitive Exposition of The Rule Against Perpetuities and the Fertile Octogenarian" This snoozer has put generations of law students to sleep.
The rule is deceptively simple:
"All transfers of property, real or personal, must vest in lives in being plus 21 years"
The only part of the rule that is seemingly not confusing is the 21 years, but we have to include, if applicable, gestation periods in utero and unborn widows (don't ask).
The origin of the rule is the 1682 case of the Duke of Norfolk. Without this case, the Duke would have deservedly remained a complete historical nonentity. Instead, we are blest with The Rule Against Perpetuities, which has struck down more common law beneficiaries than the Plague.
You can make any subject interesting by throwing in a little sex, which brings us to the mythical "Fertile Octogenarian." Since life expectancy in Merrie Olde England was far short of 40, and Viagra did not exist, the thought of an olde geezer getting it on with a fair wench (Cf. Restoration Comedies) was an inspiration for all men, from the highest lord to the lowly, illiterate peasant.
Even in our modern world, the Fertile Octogenarian rarely exists. Tony Randall begat, or is it begot, at 77 and 78. The billionaire Kirk Kerkorian initially hit the jackpot at 82 during his 28 day marriage to Lisa Bonder. But Kirk did not acquire his billions by lying down. Instead he retained the now disgraced and felonious PI Anthony Pellicano to rummage through garbage and prove with retrieved DNA (unknown to the Duke of Norfolk) that the half-billionaire Steve Bing was the biological father of Kira Rose Kirkorian.
Depending on the moment of conception, Kirk may have been cuckolded (another olde English concept) by Steve. Kirk, unlike Sir Paul McCartney, believed in a prenup. If only the Duke of Norfolk could have availed himself of this 20th Century American construct. What is the present value of 28 days in bed? Certain not the £24.3 million Heather Mills earned from Sir Paul. Both Kirk and Paul have learnt that money can't buy you love.
But I digress to make it interesting.
Even with the Hollywood investments of Bing and Kirkorian, Hollywood cannot immortalize the Rule against Perpetuities. Gidget Gets a Perpetuity just doesn't cut it.
Senator Sperm Thurmond came close as his youngest child was born when the Senator was a sprite young 74. He was active for over 5 decades on many fronts.
A Harvard Law Professor, W. Barton Leach, wrote the definitive explanation for law students in the 1938 Harvard Law Review. The 30 page "Perpetuities in a Nutshell" is a sure cure for insomnia. No medications are necessary, which should extend your perpetuities period.
The 1938 article was not an unbridled success. He followed 27 years later in the Harvard Law Review with "Perpetuities: The Nutshell Revisited," which provides yet another treatment for insomnia.
Leach was subsequently befuddled by the issue of sperm banks and perpetuities. We now have to deal with frozen eggs and sperm and fertile octogenarians, who may overlap like Venn diagrams. What a disgusting thought!
Leach has survived in the annals of legal education by allowing female students to speak only on Ladies Day.
Needless to say, Harvard Law School's reputation and $billion endowment was not based on Leach or perpetuities. Harvard and Yale thrive on legacies, to which the Rule against Perpetuities is inapplicable.
On the opposite coast, the California Supreme Court held in 1961 that a lawyer did not commit malpractice in failing to understand the Rule Against Perpetuities in drafting a will, thereby leaving the cut-off beneficiaries without a legal remedy. Clearly, if the judges did not understand the Rule as students, they were not going to perpetuate it on younger generations.
For further reading on the Rule Against Perpetuities you are referred to the classic 1886 third edition of John Chipman Gray's Rule Against Perpetuities. The hardbound, which has long exceeded the perpetuities period, is available on line from the discounter Target for only $125.
The ostensible reason for the Rule was a fear of the dead hand of the donor controlling the future.
The 13 Original Colonies achieved independence from England in 1783 in the Treaty of Paris. 26 countries when Ireland threw off the British yoke in 1921. India acquired independence in 1947. Australia, Canada, South Africa, and New Zealand achieved independence before India.
Yet all of these independent countries are still bound by the dead hand of the defunct British Empire. Only Alaska, Idaho, New Jersey, and South Dakota have cut the Gordian Knot (an even older Greek concept) and abrogated the Rule Against Perpetuities.
You now know everything about perpetuities except that which will be tested on the Bar.
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