### Question:
WeightedInput implements Comparable<WeightedInput>, SimilarityInput { public SimilarityResult cosineSquaresSkip(RleDecoder decoder, double similarityCutoff, WeightedInput other, double skipValue, boolean bidirectional) { double[] thisWeights = weights; double[] otherWeights = other.weights; if (decoder != null) { decoder.reset(weights, other.weights); thisWeights = decoder.item1(); otherWeights = decoder.item2(); } int len = Math.min(thisWeights.length, otherWeights.length); double cosineSquares = Intersections.cosineSquareSkip(thisWeights, otherWeights, len, skipValue); long intersection = 0; if (similarityCutoff >= 0D && (cosineSquares == 0 || cosineSquares < similarityCutoff)) return null; return new SimilarityResult(id, other.id, itemCount, other.itemCount, intersection, cosineSquares, bidirectional, false); } WeightedInput(long id, double[] weights, int fullSize, int itemCount);  WeightedInput(long id, double[] weights, double skipValue);  WeightedInput(long id, double[] weights); static WeightedInput sparse(long id, double[] weights, int fullSize, int compressedSize); static WeightedInput dense(long id, double[] weights, double skipValue); static WeightedInput dense(long id, double[] weights); static WeightedInput[] prepareDenseWeights(List<Map<String, Object>> data, long degreeCutoff, Double skipValue); int compareTo(WeightedInput o); SimilarityResult sumSquareDeltaSkip(RleDecoder decoder, double similarityCutoff, WeightedInput other, double skipValue, boolean bidirectional); SimilarityResult sumSquareDelta(RleDecoder decoder, double similarityCutoff, WeightedInput other, boolean bidirectional); SimilarityResult cosineSquaresSkip(RleDecoder decoder, double similarityCutoff, WeightedInput other, double skipValue, boolean bidirectional); SimilarityResult cosineSquares(RleDecoder decoder, double similarityCutoff, WeightedInput other, boolean bidirectional); SimilarityResult pearson(RleDecoder decoder, double similarityCutoff, WeightedInput other, boolean bidirectional); SimilarityResult pearsonSkip(RleDecoder decoder, double similarityCutoff, WeightedInput other, Double skipValue, boolean bidirectional); @Override long getId(); int initialSize();  }### Answer:
@Test void cosineSkipNoCompression() { double[] weights1 = new double[]{1, 2, 3, 4, 4, 4, 4, 5, 6}; double[] weights2 = new double[]{1, 2, 3, 4, 4, 4, 4, 5, 6}; WeightedInput input1 = new WeightedInput(1, weights1); WeightedInput input2 = new WeightedInput(2, weights2); SimilarityResult similarityResult = input1.cosineSquaresSkip(null, -1.0, input2, 0.0, true); assertEquals(1.0, similarityResult.similarity, 0.01); }
@Test void cosineSkipCompression() { double[] weights1 = new double[]{1, 2, 3, 4, 4, 4, 4, 5, 6, 0, 0, 0, 0}; double[] weights2 = new double[]{1, 2, 3, 4, 4, 4, 4, 5, 6, 0, 0, 0, 0}; WeightedInput input1 = new WeightedInput(1, Weights.buildRleWeights(weights1, 3)); WeightedInput input2 = new WeightedInput(2, Weights.buildRleWeights(weights2, 3)); RleDecoder decoder = new RleDecoder(weights1.length); SimilarityResult similarityResult = input1.cosineSquaresSkip(decoder, -1.0, input2, 0.0, true); assertEquals(1.0, similarityResult.similarity, 0.01); }