Computes normalization when points are contained in a list of lists
@param points Input: List of observed points. Not modified.
@param normalize Output: 3x3 normalization matrix for first set of points. Modified.
428465
public static void computeNormalizationLL(List<List<Point2D_F64>> points, NormalizationPoint2D normalize )
	{
		double meanX = 0;
		double meanY = 0;

		int count = 0;

		for (int i = 0; i < points.size(); i++) {
			List<Point2D_F64> l = points.get(i);
			for (int j = 0; j < l.size(); j++) {
				Point2D_F64 p = l.get(j);
				meanX += p.x;
				meanY += p.y;
			}
			count += l.size();
		}

		meanX /= count;
		meanY /= count;

		double stdX = 0;
		double stdY = 0;

		for (int i = 0; i < points.size(); i++) {
			List<Point2D_F64> l = points.get(i);
			for (int j = 0; j < l.size(); j++) {
				Point2D_F64 p = l.get(j);
				double dx = p.x - meanX;
				double dy = p.y - meanY;
				stdX += dx*dx;
				stdY += dy*dy;
			}
		}

		normalize.meanX = meanX;
		normalize.meanY = meanY;

		normalize.stdX = Math.sqrt(stdX/count);
		normalize.stdY = Math.sqrt(stdY/count);
	}
65670
public static void normalize(AssociativeArray associativeArray) {
        double sum = 0.0;
        //Prevents numeric underflow by subtracting the max. References: http://www.youtube.com/watch?v=-RVM21Voo7Q
        for(Map.Entry<Object, Object> entry : associativeArray.entrySet()) {
            Double value = TypeInference.toDouble(entry.getValue());
            associativeArray.put(entry.getKey(), value);

            sum += value;
        }
        
        if(sum!=0.0) {
            for(Map.Entry<Object, Object> entry : associativeArray.entrySet()) {
                associativeArray.put(entry.getKey(), TypeInference.toDouble(entry.getValue())/sum);
            }
        }
    }
194055
public static Geometry normalize(Geometry geometry) {
        if(geometry == null){
            return null;
        }
        geometry.normalize();
        return geometry;
    }
87693
private List<Object> normalizeList(List<Object> list) {
    	List<Object> result = new ArrayList<Object>();
    	for (Object o : list) {
    		if (o == null) {
    			result.add(nullLiteral);
    		} else if (o instanceof Boolean) {
    			result.add(((Boolean) o).toString());
    		} else if (o instanceof List<?>) {
    			result.add(normalizeList((List<Object>) o));
    		} else if (o instanceof Map<?, ?>) {
    			result.add(normalizeMap((Map<String, Object>) o));
    		} else if (!(o instanceof Number)) {
    			result.add(o);
    		}
    	}
    	return result;
    }
990198
def normalize(self):
        
        if self.should_collapse(): return self.collapse()
        elif self.number_of_values == 1: return list(self._si_set)[0]
        else:
            for si in self._si_set:
                self._update_bits(si)
            return self
800681
public static <T> List<Optional<LocalProperty<T>>> normalize(List<? extends LocalProperty<T>> localProperties)
    {
        List<Optional<LocalProperty<T>>> normalizedProperties = new ArrayList<>(localProperties.size());
        Set<T> constants = new HashSet<>();
        for (LocalProperty<T> localProperty : localProperties) {
            normalizedProperties.add(localProperty.withConstants(constants));
            constants.addAll(localProperty.getColumns());
        }
        return normalizedProperties;
    }
643591
public void setFromPoints(Point2D[] points) {
		if (points == null || points.length == 0) {
			setEmpty();
			return;
		}

		Point2D pt = points[0];
		setCoords(pt.x, pt.y);
		for (int i = 1; i < points.length; i++) {
			Point2D pt2d = points[i];
			mergeNE(pt2d.x, pt2d.y);
		}
	}
461253
function (points, width) {
		// points format: [M, 0, 0, L, 100, 0]
		// normalize to a crisp line
		if (points[1] === points[4]) {
			points[1] = points[4] = mathRound(points[1]) + (width % 2 / 2);
		}
		if (points[2] === points[5]) {
			points[2] = points[5] = mathRound(points[2]) + (width % 2 / 2);
		}
		return points;
	}
359992
public List<? extends Solution<?>> normalize(List<? extends Solution<?>> solutionList) {
    Front normalizedFront ;
    if (solutionList == null) {
      throw new JMetalException("The front is null") ;
    }

    normalizedFront = getNormalizedFront(new ArrayFront(solutionList), maximumValues, minimumValues);

    return FrontUtils.convertFrontToSolutionList(normalizedFront) ;
  }
1126624
final void normalizeRowOrder() {
    Collections.sort(rows, (a,b) -> Integer.compare(a.hashCode(), b.hashCode()));    
  }
411604
public Matrix4x3d normalize3x3(Matrix4x3d dest) {
        double invXlen = 1.0 / Math.sqrt(m00 * m00 + m01 * m01 + m02 * m02);
        double invYlen = 1.0 / Math.sqrt(m10 * m10 + m11 * m11 + m12 * m12);
        double invZlen = 1.0 / Math.sqrt(m20 * m20 + m21 * m21 + m22 * m22);
        dest.m00 = m00 * invXlen; dest.m01 = m01 * invXlen; dest.m02 = m02 * invXlen;
        dest.m10 = m10 * invYlen; dest.m11 = m11 * invYlen; dest.m12 = m12 * invYlen;
        dest.m20 = m20 * invZlen; dest.m21 = m21 * invZlen; dest.m22 = m22 * invZlen;
        return dest;
    }
1109210
private void init( List<CalibrationObservation> observations )
	{
		int totalPoints = 0;
		for (int i = 0; i < observations.size(); i++) {
			totalPoints += observations.get(i).size();
		}

		A.reshape(2*totalPoints,X.numRows,false);
		B.reshape(A.numRows,1,false);
	}
758824
def get_normed_points(point_array, norm): # good to go
    
    norm = float(norm)
    #floated_array = []
    #for p in point_array: # need to make sure each point is a float
        #floated_array.append(float(p))
    points = old_div(numpy.array(point_array), norm)
    return points
304175
public static double[] normalizeEquals(final double[] v1) {
    final double norm = 1. / euclideanLength(v1);
    if(norm < Double.POSITIVE_INFINITY) {
      for(int row = 0; row < v1.length; row++) {
        v1[row] *= norm;
      }
    }
    return v1;
  }
808374
private void normalizeNodeTypes(Node n) {
    normalizeBlocks(n);

    for (Node child = n.getFirstChild();
         child != null; child = child.getNext()) {
      // This pass is run during the CompilerTestCase validation, so this
      // parent pointer check serves as a more general check.
      checkState(child.getParent() == n);

      normalizeNodeTypes(child);
    }
  }
1355630
public void write(@NonNull MultiNormalizerStandardize normalizer, @NonNull OutputStream stream) throws IOException {
        try (DataOutputStream dos = new DataOutputStream(stream)) {
            dos.writeBoolean(normalizer.isFitLabel());
            dos.writeInt(normalizer.numInputs());
            dos.writeInt(normalizer.isFitLabel() ? normalizer.numOutputs() : -1);

            for (int i = 0; i < normalizer.numInputs(); i++) {
                Nd4j.write(normalizer.getFeatureMean(i), dos);
                Nd4j.write(normalizer.getFeatureStd(i), dos);
            }
            if (normalizer.isFitLabel()) {
                for (int i = 0; i < normalizer.numOutputs(); i++) {
                    Nd4j.write(normalizer.getLabelMean(i), dos);
                    Nd4j.write(normalizer.getLabelStd(i), dos);
                }
            }
            dos.flush();
        }
    }
392513
private boolean checkHaworthAlignment(Point2d[] points) {
        
        for (int i = 0; i < points.length; i++) {
            Point2d curr = points[i];
            Point2d next = points[(i+1) % points.length];
            
            double deltaY = curr.y - next.y;
            
            if (Math.abs(deltaY) < CARDINALITY_THRESHOLD)
                return true;
        }
        
        return false;
    }