The muHVT package is a collection of R functions for vector quantization and construction of hierarchical voronoi tessellations as a data visualization tool to visualize cells using quantization. The hierarchical cells are computed using Hierarchical K-means where a quantization threshold governs the levels in the hierarchy for a set \(k\) parameter (the maximum number of cells at each level). The package is particularly helpful to visualize rich mutlivariate data.

This package additionally provides functions for computing the Sammon’s projection and plotting the heat map of the variables on the tiles of the tessellations.

This package performs vector quantization using the following algorithm -

- Hierarchical Vector Quantization using \(k-means\)

- The k-means algorithm randomly selects
*k*data points as initial means *k*clusters are formed by assigning each data point to its closest cluster mean using the Euclidean distance- Virtual means for each cluster are calculated by using all datapoints contained in a cluster

The second and third steps are iterated until a predefined number of iterations is reached or the clusters converge. The runtime for the algorithm is O(n).

The algorithm divides the dataset recursively into cells. The \(k-means\) algorithm is used by setting \(k\) to, say two, in order to divide the dataset into two subsets. These two subsets are further divided into two subsets by setting \(k\) to two, resulting in a total of four subsets. The recursion terminates when the cells either contain a single data point or a stop criterion is reached. In this case, the stop criterion is set to when the cell error exceeds the quantization threshold.

The steps for this method are as follows :

- Select k(number of cells), depth and quantization error threshold
- Perform k-means on the input dataset
- Calculate quantization error for each of the k cells
- Compare the quantization error for each cell to quantization error threshold
- Repeat steps 2 to 4 for each of the k cells whose quantization error is above threshold until stop criterion is reached.

The stop criterion is when the quantization error of a cell satisfies one of the below conditions

- reaches below quantization error threshold
- there is a single point in the cell
- the user specified depth has been attained

The quantization error for a cell is defined as follows :

\[QE = \max_i(||A-F_i||_{p})\]

where

- \(A\) is the centroid of the cell
- \(F_i\) represents a data point in the cell
- \(m\) is the number of points in the cell
- \(p\) is the \(p\)-norm metric. Here \(p\) = 1 represents L1 Norm and \(p\) = 2 represents L2 Norm.

Let us try to understand quantization error with an example.

An example of a 2 dimensional VQ is shown above.

In the above image, we can see 5 cells with each cell containing a certain number of points. The centroid for each cell is shown in blue. These centroids are also known as codewords since they represent all the points in that cell. The set of all codewords is called a codebook.

Now we want to calculate quantization error for each cell. For the sake of simplicity, let’s consider only one cell having centroid `A`

and `m`

data points \(F_i\) for calculating quantization error.

For each point, we calculate the distance between the point and the centroid.

\[ d = ||A - F_i||_{p} \]

In the above equation, p = 1 means `L1_Norm`

distance whereas p = 2 means `L2_Norm`

distance. In the package, the `L1_Norm`

distance is chosen by default. The user can pass either `L1_Norm`

, `L2_Norm`

or a custom function to calculate the distance between two points in n dimensions.

\[QE = \max_i(||A-F_i||_{p})\]

Now, we take the maximum calculated distance of all m points. This gives us the furthest distance of a point in the cell from the centroid, which we refer to as `Quantization Error`

. If the Quantization Error is higher than the given threshold, the centroid/codevector is not a good representation for the points in the cell. Now we can perform further Vector Quantization on these points and repeat the above steps.

Please note that the user can select mean, max or any custom function to calculate the Quantization Error. The custom function takes a vector of m value (where each value is a distance between point in `n`

dimensions and centroids) and returns a single value which is the Quantization Error for the cell.

If we select `mean`

as the error metric, the above Quantization Error equation will look like this :

\[QE = \frac{1}{m}\sum_{i=1}^m||A-F_i||_{p}\]

A Voronoi diagram is a way of dividing space into a number of regions. A set of points (called seeds, sites, or generators) is specified beforehand and for each seed, there will be a corresponding region consisting of all points within proximity of that seed. These regions are called Voronoi cells. It is complementary to Delaunay triangulation.

Sammon’s projection is an algorithm that maps a high-dimensional space to a space of lower dimensionality while attempting to preserve the structure of inter-point distances in the projection. It is particularly suited for use in exploratory data analysis and is usually considered a non-linear approach since the mapping cannot be represented as a linear combination of the original variables. The centroids are plotted in 2D after performing Sammon’s projection at every level of the tessellation.

Denoting the distance between \(i^{th}\) and \(j^{th}\) objects in the original space by \(d_{ij}^*\), and the distance between their projections by \(d_{ij}\). Sammon’s mapping aims to minimize the below error function, which is often referred to as Sammon’s stress or Sammon’s error

\[E=\frac{1}{\sum_{i<j} d_{ij}^*}\sum_{i<j}\frac{(d_{ij}^*-d_{ij})^2}{d_{ij}^*}\]

The minimization of this can be performed either by gradient descent, as proposed initially, or by other means, usually involving iterative methods. The number of iterations need to be experimentally determined and convergent solutions are not always guaranteed. Many implementations prefer to use the first Principal Components as a starting configuration.

In this package, we use `sammons`

from the package `MASS`

to project higher dimensional data to a 2D space. The function `hvq`

called from the `HVT`

function returns hierarchical quantized data which will be the input for construction of the tesselations. The data is then represented in 2D coordinates and the tessellations are plotted using these coordinates as centroids. We use the package `deldir`

for this purpose. The `deldir`

package computes the Delaunay triangulation (and hence the Dirichlet or Voronoi tesselation) of a planar point set according to the second (iterative) algorithm of Lee and Schacter. For subsequent levels, transformation is performed on the 2D coordinates to get all the points within its parent tile. Tessellations are plotted using these transformed points as centroids. The lines in the tessellations are chopped in places so that they do not protrude outside the parent polygon. This is done for all the subsequent levels.

In this section, we will use the `Prices of Personal Computers`

dataset. This dataset contains 6259 observations and 10 features. The dataset observes the price from 1993 to 1995 of 486 personal computers in the US. The variables are price, speed, ram, screen, cd, etc. The dataset can be downloaded from here.

In this example, we will compress this dataset by using hierarhical VQ via k-means and visualize the Voronoi Tesselation plots using Sammons projection. Later on, we will overlay price, speed and screen variables as a heatmap to generate further insights.

Here, we load the data and store into a variable `computers`

.

```
set.seed(240)
# Load data from csv files
computers <- read.csv("https://raw.githubusercontent.com/SangeetM/dataset/master/Computers.csv")
```

Let’s have a look at some of the data

X | price | speed | hd | ram | screen | cd | multi | premium | ads | trend |
---|---|---|---|---|---|---|---|---|---|---|

1 | 1499 | 25 | 80 | 4 | 14 | no | no | yes | 94 | 1 |

2 | 1795 | 33 | 85 | 2 | 14 | no | no | yes | 94 | 1 |

3 | 1595 | 25 | 170 | 4 | 15 | no | no | yes | 94 | 1 |

4 | 1849 | 25 | 170 | 8 | 14 | no | no | no | 94 | 1 |

5 | 3295 | 33 | 340 | 16 | 14 | no | no | yes | 94 | 1 |

6 | 3695 | 66 | 340 | 16 | 14 | no | no | yes | 94 | 1 |

Now let us check the structure of the data

```
str(computers)
#> 'data.frame': 6259 obs. of 11 variables:
#> $ X : int 1 2 3 4 5 6 7 8 9 10 ...
#> $ price : int 1499 1795 1595 1849 3295 3695 1720 1995 2225 2575 ...
#> $ speed : int 25 33 25 25 33 66 25 50 50 50 ...
#> $ hd : int 80 85 170 170 340 340 170 85 210 210 ...
#> $ ram : int 4 2 4 8 16 16 4 2 8 4 ...
#> $ screen : int 14 14 15 14 14 14 14 14 14 15 ...
#> $ cd : Factor w/ 2 levels "no","yes": 1 1 1 1 1 1 2 1 1 1 ...
#> $ multi : Factor w/ 2 levels "no","yes": 1 1 1 1 1 1 1 1 1 1 ...
#> $ premium: Factor w/ 2 levels "no","yes": 2 2 2 1 2 2 2 2 2 2 ...
#> $ ads : int 94 94 94 94 94 94 94 94 94 94 ...
#> $ trend : int 1 1 1 1 1 1 1 1 1 1 ...
```

Let’s get a summary of the data

```
summary(computers)
#> X price speed hd
#> Min. : 1 Min. : 949 Min. : 25.00 Min. : 80.0
#> 1st Qu.:1566 1st Qu.:1794 1st Qu.: 33.00 1st Qu.: 214.0
#> Median :3130 Median :2144 Median : 50.00 Median : 340.0
#> Mean :3130 Mean :2220 Mean : 52.01 Mean : 416.6
#> 3rd Qu.:4694 3rd Qu.:2595 3rd Qu.: 66.00 3rd Qu.: 528.0
#> Max. :6259 Max. :5399 Max. :100.00 Max. :2100.0
#> ram screen cd multi premium
#> Min. : 2.000 Min. :14.00 no :3351 no :5386 no : 612
#> 1st Qu.: 4.000 1st Qu.:14.00 yes:2908 yes: 873 yes:5647
#> Median : 8.000 Median :14.00
#> Mean : 8.287 Mean :14.61
#> 3rd Qu.: 8.000 3rd Qu.:15.00
#> Max. :32.000 Max. :17.00
#> ads trend
#> Min. : 39.0 Min. : 1.00
#> 1st Qu.:162.5 1st Qu.:10.00
#> Median :246.0 Median :16.00
#> Mean :221.3 Mean :15.93
#> 3rd Qu.:275.0 3rd Qu.:21.50
#> Max. :339.0 Max. :35.00
```

Let us first split the data into train and test. We will use 80% of the data as train and remaining as test.

```
noOfPoints <- dim(computers)[1]
trainLength <- as.integer(noOfPoints * 0.8)
trainComputers <- computers[1:trainLength,]
testComputers <- computers[(trainLength+1):noOfPoints,]
```

K-means is not suitable for factor variables as the sample space for factor variables is discrete. A Euclidean distance function on such a space isn’t really meaningful. Hence, we will delete the factor variables in our dataset.

Here we keep the original `trainComputers`

and `testComputers`

as we will use the price variable from this dataset to overlay as heatmap and generate some insights.

```
trainComputers <- trainComputers %>% dplyr::select(-c(X,cd,multi,premium,trend))
testComputers <- testComputers %>% dplyr::select(-c(X,cd,multi,premium,trend))
```

Let us try to understand the HVT function first.

Each of the parameters have been explained below

- A dataframe with numeric columns`dataset`

- An integer indicating the number of cells per hierarchy (level)`nlcust`

- An integer indicating the number of levels. (1 = No hierarchy, 2 = 2 levels, etc …)`depth`

- A number indicating the quantization error threshold. A cell will only breakdown into further cells if the quantization error of the cell is above the defined quantization error threshold`quant.error`

- A number indicating the scale factor for the tesselations so as to visualize the sub-tesselations efficiently`projection.scale`

- A logical value indicating whether the columns in your dataset need to be normalized. Default value is TRUE. The algorithm supports Z-score normalization`normalize`

- The distance metric can be`distance_metric`

`L1_Norm`

or`L2_Norm`

.`L1_Norm`

is selected by default. The distance metric is used to calculate the distance between an`n`

dimensional point and centroid. The user can also pass a custom function to calculate this distance- The error metric can be`error_metric`

`mean`

or`max`

.`max`

is selected by default.`max`

will return the max of`m`

values and`mean`

will take mean of`m`

values where each value is a distance between a point and centroid of the cell. Moreover, the user can also pass a custom function to calculate the error metric

First we will perform hierarchical Vector Quantization at level 1 by setting the parameter depth to 1 and the number of cells to 15. Here, level 1 signifies no hierarchy.

```
set.seed(240)
hvt.results <- list()
hvt.results <- HVT(trainComputers,
nclust = 15,
depth = 1,
quant.err = 0.2,
projection.scale = 10,
normalize = T,
distance_metric = "L1_Norm",
error_metric = "mean")
```

Now let’s try to understand plotHVT function. The parameters have been explained in detail below

- A list containing the ouput of the HVT function which has the details of the tessellations to be plotted`hvt.results`

- A vector indicating the line widths of the tessellation boundaries for each level`line.width`

- A vector indicating the colors of the tessellations boundaries at each level`color.vec`

- Symbol type of the centroids of the tessellations (parent levels). Refer points (default = 21)`pch1`

- Size of centroids of first level tessellations (default = 3)`centroid.size`

- Set a title for the plot (default = NULL)`title`

Let’s plot the voronoi tesselation

```
# Voronoi tesselation plot for level one
plotHVT(hvt.results,
line.width = c(1.2),
color.vec = c("#141B41"),
maxDepth = 1)
```

As per the manual, ** hvt.results[[3]]** gives us detailed information about the hierarchical vector quantized data.

** hvt.results[[3]][['summary']]** gives a nice tabular data containing no of points, Quantization Error and the codebook.

Now let us understand what each column in the summary table means

- Level of the cell. In this case, we have performed Vector Quantization for depth 1. Hence Segment Level is 1`Segment Level`

- Parent segment of the cell`Segment Parent`

- The children of a particular cell. In this case, first level has 15 cells hence we can see Segment Child 1,2,3,4,5 ,..,15.`Segment Child`

- No of points in each cell`n`

- Quantization Error for each cell`Quant.Error`

All the columns after this will contain centroids for each cell. They can also be called a codebook, which represents a collection of all centroids or codewords.

Segment.Level | Segment.Parent | Segment.Child | n | Quant.Error | price | speed | hd | ram | screen | ads |
---|---|---|---|---|---|---|---|---|---|---|

1 | 1 | 1 | 480 | 0.33 | 0.69 | 0.70 | 0.24 | -0.02 | 0.06 | 0.57 |

1 | 1 | 2 | 390 | 0.49 | 0.83 | 0.21 | 0.05 | 0.10 | 2.88 | 0.10 |

1 | 1 | 3 | 145 | 0.35 | 0.27 | 2.67 | 0.17 | -0.20 | -0.17 | 0.71 |

1 | 1 | 4 | 505 | 0.26 | -0.17 | -0.80 | 0.24 | -0.04 | -0.31 | 0.42 |

1 | 1 | 5 | 241 | 0.28 | -0.34 | 0.66 | -0.73 | -0.75 | -0.40 | -0.40 |

1 | 1 | 6 | 150 | 0.49 | 0.90 | -0.55 | 2.71 | 2.32 | 0.29 | -0.60 |

1 | 1 | 7 | 286 | 0.23 | 0.75 | -0.71 | 0.79 | 1.61 | -0.41 | 0.35 |

1 | 1 | 8 | 258 | 0.3 | -0.39 | 0.76 | 0.71 | 0.00 | -0.16 | -0.54 |

1 | 1 | 9 | 324 | 0.25 | -1.08 | -0.79 | -0.56 | -0.69 | -0.38 | -0.76 |

1 | 1 | 10 | 401 | 0.29 | -0.54 | 0.56 | -0.62 | -0.76 | -0.32 | 0.76 |

1 | 1 | 11 | 288 | 0.34 | 1.19 | 1.24 | 0.74 | 1.61 | 0.13 | 0.38 |

1 | 1 | 12 | 917 | 0.22 | -0.98 | -0.91 | -0.82 | -0.77 | -0.44 | 0.55 |

1 | 1 | 13 | 229 | 0.45 | 1.09 | 0.33 | -0.16 | 0.33 | -0.15 | -1.94 |

1 | 1 | 14 | 97 | 0.57 | 2.01 | 1.24 | 3.36 | 2.46 | 0.20 | 0.01 |

1 | 1 | 15 | 296 | 0.29 | -0.33 | -0.53 | -0.81 | -0.51 | -0.43 | -2.16 |

Let’s have a look at `Quant.Error`

variable in the above table. It seems that none of the cells have hit the quantization threshold error.

Now let’s check the compression summary. The table below shows no of cells, no of cells having quantization error below threshold and percentage of cells having quantization error below threshold for each level.

segmentLevel | noOfCells | noOfCellsBelowQuantizationError | percentOfCellsBelowQuantizationErrorThreshold |
---|---|---|---|

1 | 15 | 0 | 0 |

As it can be seen in the table above, percentage of cells in level 1 having Quantization Error below threshold is `0%`

. Hence, we can go one level deeper and try to compress it further.

We will now overlay the `Quant.Error`

variable as heatmap over the Voronoi Tesselation plot to visualize the quantization error better.

Let’s have look at the function `hvtHmap`

which we will use to overlay a variable as heatmap.

- A list of hvt.results obtained from the HVT function`hvt.results`

- A dataframe containing the variables to overlay as a heatmap. The user can pass an external dataset or the dataset that was used to perform hierarchical vector quantization. The dataset should have the same number of points as the dataset used to perform hierarchical Vector Quantization in the HVT function`dataset`

- A number indicating the level for which the heat map is to be plotted`child.level`

- The column number of column name from the dataset indicating the variables for which the heat map is to be plotted. To plot the quantization error as heatmap, pass`hmap.cols`

`'quant_error'`

. Similary to plot the no of points in each cell as heatmap, pass`'no_of_points'`

as a parameter- A color vector such that length(color.vec) = child.level (default = NULL)`color.vec`

- A line width vector such that length(line.width) = child.level (default = NULL)`line.width`

- A number indicating the heat map color palette. 1 - rainbow, 2 - heat.colors, 3 - terrain.colors, 4 - topo.colors, 5 - cm.colors, 6 - BlCyGrYlRd (Blue,Cyan,Green,Yellow,Red) color (default = 6)`palette.color`

- A boolean indicating whether the centroids should be plotted on the tesselations (default = FALSE)`show.points`

Now let’s plot the quantization error for each cell at level one as a heatmap.

```
hvtHmap(hvt.results,
trainComputers,
child.level = 1,
hmap.cols = "Quant.Error",
line.width = c(0.2),
color.vec = c("#141B41"),
palette.color = 6,
centroid.size = 3,
show.points = T,
quant.error.hmap = 0.2,
nclust.hmap = 15)
```

Now let’s go one level deeper and perform hierarchical vector quantization.

```
set.seed(240)
hvt.results2 <- list()
# depth=2 is used for level2 in the hierarchy
hvt.results2 <- HVT(trainComputers,
nclust = 15,
depth = 2,
quant.err = 0.2,
projection.scale = 10,
normalize = T,
distance_metric = "L1_Norm",
error_metric = "mean")
```

Let’s plot the voronoi tesselation for both the levels.

```
# Voronoi tesselation plot for level two
plotHVT(hvt.results2,
line.width = c(1.2, 0.8),
color.vec = c("#141B41","#0582CA"),
maxDepth = 2)
```

In the table below, Segment Level signifies the depth.

Level 1 has 15 cells

Level 2 has 225 cells .i.e. each cell in level 1 is divided into 15 cells each

Let’s analyze the summary table again for `Quant.Error`

and see if any of the cells in the 2nd level have Quantization Error below the Quantization Error threshold. In the table below, the values for `Quant.Error`

of the cells which have hit the Quantization Error threshold are shown in red. Here we are showing just top 50 rows for the sake of brevity.

Segment.Level | Segment.Parent | Segment.Child | n | Quant.Error | price | speed | hd | ram | screen | ads |
---|---|---|---|---|---|---|---|---|---|---|

1 | 1 | 1 | 480 | 0.33 | 0.69 | 0.70 | 0.24 | -0.02 | 0.06 | 0.57 |

1 | 1 | 2 | 390 | 0.49 | 0.83 | 0.21 | 0.05 | 0.10 | 2.88 | 0.10 |

1 | 1 | 3 | 145 | 0.35 | 0.27 | 2.67 | 0.17 | -0.20 | -0.17 | 0.71 |

1 | 1 | 4 | 505 | 0.26 | -0.17 | -0.80 | 0.24 | -0.04 | -0.31 | 0.42 |

1 | 1 | 5 | 241 | 0.28 | -0.34 | 0.66 | -0.73 | -0.75 | -0.40 | -0.40 |

1 | 1 | 6 | 150 | 0.49 | 0.90 | -0.55 | 2.71 | 2.32 | 0.29 | -0.60 |

1 | 1 | 7 | 286 | 0.23 | 0.75 | -0.71 | 0.79 | 1.61 | -0.41 | 0.35 |

1 | 1 | 8 | 258 | 0.3 | -0.39 | 0.76 | 0.71 | 0.00 | -0.16 | -0.54 |

1 | 1 | 9 | 324 | 0.25 | -1.08 | -0.79 | -0.56 | -0.69 | -0.38 | -0.76 |

1 | 1 | 10 | 401 | 0.29 | -0.54 | 0.56 | -0.62 | -0.76 | -0.32 | 0.76 |

1 | 1 | 11 | 288 | 0.34 | 1.19 | 1.24 | 0.74 | 1.61 | 0.13 | 0.38 |

1 | 1 | 12 | 917 | 0.22 | -0.98 | -0.91 | -0.82 | -0.77 | -0.44 | 0.55 |

1 | 1 | 13 | 229 | 0.45 | 1.09 | 0.33 | -0.16 | 0.33 | -0.15 | -1.94 |

1 | 1 | 14 | 97 | 0.57 | 2.01 | 1.24 | 3.36 | 2.46 | 0.20 | 0.01 |

1 | 1 | 15 | 296 | 0.29 | -0.33 | -0.53 | -0.81 | -0.51 | -0.43 | -2.16 |

2 | 1 | 1 | 41 | 0.17 | 0.98 | 0.88 | -0.31 | -0.18 | 0.55 | 0.44 |

2 | 1 | 2 | 55 | 0.11 | 0.48 | 0.92 | 0.59 | 0.05 | 0.55 | 0.54 |

2 | 1 | 3 | 57 | 0.13 | 0.40 | 0.08 | -0.02 | 0.01 | 0.55 | 0.51 |

2 | 1 | 4 | 22 | 0.19 | 1.95 | 0.77 | 0.68 | -0.01 | -0.61 | 0.58 |

2 | 1 | 5 | 53 | 0.11 | 0.78 | 0.92 | 0.31 | 0.03 | -0.61 | 0.70 |

2 | 1 | 6 | 45 | 0.13 | 0.15 | 1.03 | 0.34 | -0.01 | -0.61 | 1.04 |

2 | 1 | 7 | 31 | 0.17 | 0.85 | 0.81 | 0.00 | -0.16 | -0.61 | 0.01 |

2 | 1 | 8 | 28 | 0.15 | -0.01 | 0.70 | 0.42 | 0.04 | 0.55 | 1.37 |

2 | 1 | 9 | 50 | 0.12 | 0.51 | 0.09 | 0.19 | 0.00 | -0.61 | 0.62 |

2 | 1 | 10 | 35 | 0.18 | 2.13 | 0.87 | 0.64 | -0.16 | 0.55 | 0.48 |

2 | 1 | 11 | 39 | 0.1 | -0.03 | 0.92 | -0.06 | 0.04 | 0.55 | 0.50 |

2 | 1 | 12 | 24 | 0.2 | 1.10 | 0.64 | 0.33 | 0.00 | 0.55 | -0.21 |

2 | 1 | 13 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 1 | 14 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 1 | 15 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 2 | 1 | 24 | 0.19 | 2.32 | 0.92 | 0.32 | -0.03 | 2.88 | 0.53 |

2 | 2 | 2 | 12 | 0.32 | 1.25 | -0.49 | 1.29 | 1.37 | 2.88 | -1.03 |

2 | 2 | 3 | 19 | 0.22 | 1.25 | 0.31 | -0.31 | -0.43 | 2.88 | 0.80 |

2 | 2 | 4 | 6 | 0.32 | 3.80 | 2.08 | 1.45 | 0.06 | 2.88 | -0.25 |

2 | 2 | 5 | 16 | 0.18 | 0.73 | -0.67 | -0.52 | -0.13 | 2.88 | -1.75 |

2 | 2 | 6 | 19 | 0.31 | 0.76 | 0.96 | 1.19 | 1.22 | 2.88 | -0.33 |

2 | 2 | 7 | 56 | 0.2 | -0.52 | -0.90 | -0.50 | -0.71 | 2.88 | 0.40 |

2 | 2 | 8 | 63 | 0.22 | 0.46 | -0.80 | -0.11 | -0.23 | 2.88 | 0.51 |

2 | 2 | 9 | 23 | 0.18 | 1.21 | 0.52 | -0.54 | -0.14 | 2.88 | -2.06 |

2 | 2 | 10 | 17 | 0.18 | -0.04 | 0.19 | -0.12 | -0.30 | 2.88 | 0.46 |

2 | 2 | 11 | 43 | 0.24 | 2.40 | 0.72 | 0.26 | 1.63 | 2.88 | 0.29 |

2 | 2 | 12 | 20 | 0.21 | -0.28 | 0.71 | 0.02 | -0.33 | 2.88 | -0.46 |

2 | 2 | 13 | 48 | 0.16 | 0.71 | 0.80 | 0.14 | 0.02 | 2.88 | 0.39 |

2 | 2 | 14 | 13 | 0.24 | 0.96 | 2.67 | 0.49 | 0.55 | 2.88 | 0.56 |

2 | 2 | 15 | 11 | 0.15 | 0.65 | 0.77 | 0.32 | 0.06 | 2.88 | 1.34 |

2 | 3 | 1 | 6 | 0.15 | 1.53 | 2.67 | 1.98 | 0.06 | -0.61 | -0.49 |

2 | 3 | 2 | 14 | 0.16 | 0.08 | 2.67 | 0.25 | -0.27 | -0.53 | -0.41 |

2 | 3 | 3 | 38 | 0.2 | -0.19 | 2.67 | -0.78 | -0.83 | -0.37 | 0.99 |

2 | 3 | 4 | 14 | 0.09 | 0.37 | 2.67 | 0.59 | 0.06 | -0.61 | 0.33 |

2 | 3 | 5 | 27 | 0.09 | 0.50 | 2.67 | 0.32 | 0.06 | -0.61 | 1.33 |

The users can look at the compression summary to get a quick summary on the compression as it becomes quite cumbersome to look at the summary table above as we go deeper.

segmentLevel | noOfCells | noOfCellsBelowQuantizationError | percentOfCellsBelowQuantizationErrorThreshold |
---|---|---|---|

1 | 15 | 0 | 0 |

2 | 134 | 118 | 0.88 |

As it can be seen in the table above, only `5%`

cells in the 2nd level have Quantization Error below threshold. Therefore, we can go another level deeper and try to compress the data further.

We will look at the heatmap for Quantization Error for level 2.

```
hvtHmap(hvt.results2,
trainComputers,
child.level = 2,
hmap.cols = "Quant.Error",
line.width = c(0.8,0.2),
color.vec = c("#141B41","#0582CA"),
palette.color = 6,
centroid.size = 2,
show.points = T,
quant.error.hmap = 0.2,
nclust.hmap = 15)
```

As the Quantization Error criteria is not met, let’s perform hierarchical Vector Quantization at level 3.

```
set.seed(240)
hvt.results3 <- list()
# depth=3 is used for level3 in the hierarchy
hvt.results3 <- HVT(trainComputers,
nclust = 15,
depth = 3,
quant.err = 0.2,
projection.scale = 10,
normalize = T,
distance_metric = "L1_Norm",
error_metric = "mean")
```

Let’s plot the Voronoi Tesselation for all 3 levels.

```
# Voronoi tesselation plot for level three
plotHVT(hvt.results3,
line.width = c(1.2,0.8,0.4),
color.vec = c("#141B41","#0582CA","#8BA0B4"),
centroid.size = 3,
maxDepth = 3)
```

Each of the 225 cells whose quantization is above the defined threshold in level 2 will break down into 15 cells each in level 3. Hence, as it can be seen below, level 3 has 3375 rows. So it will have 3615 rows in total. We will only show first 500 rows here.

Segment.Level | Segment.Parent | Segment.Child | n | Quant.Error | price | speed | hd | ram | screen | ads |
---|---|---|---|---|---|---|---|---|---|---|

1 | 1 | 1 | 480 | 0.33 | 0.69 | 0.70 | 0.24 | -0.02 | 0.06 | 0.57 |

1 | 1 | 2 | 390 | 0.49 | 0.83 | 0.21 | 0.05 | 0.10 | 2.88 | 0.10 |

1 | 1 | 3 | 145 | 0.35 | 0.27 | 2.67 | 0.17 | -0.20 | -0.17 | 0.71 |

1 | 1 | 4 | 505 | 0.26 | -0.17 | -0.80 | 0.24 | -0.04 | -0.31 | 0.42 |

1 | 1 | 5 | 241 | 0.28 | -0.34 | 0.66 | -0.73 | -0.75 | -0.40 | -0.40 |

1 | 1 | 6 | 150 | 0.49 | 0.90 | -0.55 | 2.71 | 2.32 | 0.29 | -0.60 |

1 | 1 | 7 | 286 | 0.23 | 0.75 | -0.71 | 0.79 | 1.61 | -0.41 | 0.35 |

1 | 1 | 8 | 258 | 0.3 | -0.39 | 0.76 | 0.71 | 0.00 | -0.16 | -0.54 |

1 | 1 | 9 | 324 | 0.25 | -1.08 | -0.79 | -0.56 | -0.69 | -0.38 | -0.76 |

1 | 1 | 10 | 401 | 0.29 | -0.54 | 0.56 | -0.62 | -0.76 | -0.32 | 0.76 |

1 | 1 | 11 | 288 | 0.34 | 1.19 | 1.24 | 0.74 | 1.61 | 0.13 | 0.38 |

1 | 1 | 12 | 917 | 0.22 | -0.98 | -0.91 | -0.82 | -0.77 | -0.44 | 0.55 |

1 | 1 | 13 | 229 | 0.45 | 1.09 | 0.33 | -0.16 | 0.33 | -0.15 | -1.94 |

1 | 1 | 14 | 97 | 0.57 | 2.01 | 1.24 | 3.36 | 2.46 | 0.20 | 0.01 |

1 | 1 | 15 | 296 | 0.29 | -0.33 | -0.53 | -0.81 | -0.51 | -0.43 | -2.16 |

2 | 1 | 1 | 41 | 0.17 | 0.98 | 0.88 | -0.31 | -0.18 | 0.55 | 0.44 |

2 | 1 | 2 | 55 | 0.11 | 0.48 | 0.92 | 0.59 | 0.05 | 0.55 | 0.54 |

2 | 1 | 3 | 57 | 0.13 | 0.40 | 0.08 | -0.02 | 0.01 | 0.55 | 0.51 |

2 | 1 | 4 | 22 | 0.19 | 1.95 | 0.77 | 0.68 | -0.01 | -0.61 | 0.58 |

2 | 1 | 5 | 53 | 0.11 | 0.78 | 0.92 | 0.31 | 0.03 | -0.61 | 0.70 |

2 | 1 | 6 | 45 | 0.13 | 0.15 | 1.03 | 0.34 | -0.01 | -0.61 | 1.04 |

2 | 1 | 7 | 31 | 0.17 | 0.85 | 0.81 | 0.00 | -0.16 | -0.61 | 0.01 |

2 | 1 | 8 | 28 | 0.15 | -0.01 | 0.70 | 0.42 | 0.04 | 0.55 | 1.37 |

2 | 1 | 9 | 50 | 0.12 | 0.51 | 0.09 | 0.19 | 0.00 | -0.61 | 0.62 |

2 | 1 | 10 | 35 | 0.18 | 2.13 | 0.87 | 0.64 | -0.16 | 0.55 | 0.48 |

2 | 1 | 11 | 39 | 0.1 | -0.03 | 0.92 | -0.06 | 0.04 | 0.55 | 0.50 |

2 | 1 | 12 | 24 | 0.2 | 1.10 | 0.64 | 0.33 | 0.00 | 0.55 | -0.21 |

2 | 1 | 13 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 1 | 14 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 1 | 15 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 2 | 1 | 24 | 0.19 | 2.32 | 0.92 | 0.32 | -0.03 | 2.88 | 0.53 |

2 | 2 | 2 | 12 | 0.32 | 1.25 | -0.49 | 1.29 | 1.37 | 2.88 | -1.03 |

2 | 2 | 3 | 19 | 0.22 | 1.25 | 0.31 | -0.31 | -0.43 | 2.88 | 0.80 |

2 | 2 | 4 | 6 | 0.32 | 3.80 | 2.08 | 1.45 | 0.06 | 2.88 | -0.25 |

2 | 2 | 5 | 16 | 0.18 | 0.73 | -0.67 | -0.52 | -0.13 | 2.88 | -1.75 |

2 | 2 | 6 | 19 | 0.31 | 0.76 | 0.96 | 1.19 | 1.22 | 2.88 | -0.33 |

2 | 2 | 7 | 56 | 0.2 | -0.52 | -0.90 | -0.50 | -0.71 | 2.88 | 0.40 |

2 | 2 | 8 | 63 | 0.22 | 0.46 | -0.80 | -0.11 | -0.23 | 2.88 | 0.51 |

2 | 2 | 9 | 23 | 0.18 | 1.21 | 0.52 | -0.54 | -0.14 | 2.88 | -2.06 |

2 | 2 | 10 | 17 | 0.18 | -0.04 | 0.19 | -0.12 | -0.30 | 2.88 | 0.46 |

2 | 2 | 11 | 43 | 0.24 | 2.40 | 0.72 | 0.26 | 1.63 | 2.88 | 0.29 |

2 | 2 | 12 | 20 | 0.21 | -0.28 | 0.71 | 0.02 | -0.33 | 2.88 | -0.46 |

2 | 2 | 13 | 48 | 0.16 | 0.71 | 0.80 | 0.14 | 0.02 | 2.88 | 0.39 |

2 | 2 | 14 | 13 | 0.24 | 0.96 | 2.67 | 0.49 | 0.55 | 2.88 | 0.56 |

2 | 2 | 15 | 11 | 0.15 | 0.65 | 0.77 | 0.32 | 0.06 | 2.88 | 1.34 |

2 | 3 | 1 | 6 | 0.15 | 1.53 | 2.67 | 1.98 | 0.06 | -0.61 | -0.49 |

2 | 3 | 2 | 14 | 0.16 | 0.08 | 2.67 | 0.25 | -0.27 | -0.53 | -0.41 |

2 | 3 | 3 | 38 | 0.2 | -0.19 | 2.67 | -0.78 | -0.83 | -0.37 | 0.99 |

2 | 3 | 4 | 14 | 0.09 | 0.37 | 2.67 | 0.59 | 0.06 | -0.61 | 0.33 |

2 | 3 | 5 | 27 | 0.09 | 0.50 | 2.67 | 0.32 | 0.06 | -0.61 | 1.33 |

2 | 3 | 6 | 46 | 0.15 | 0.39 | 2.67 | 0.47 | 0.06 | 0.55 | 0.72 |

2 | 3 | 7 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 3 | 8 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 3 | 9 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 3 | 10 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 3 | 11 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 3 | 12 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 3 | 13 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 3 | 14 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 3 | 15 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 4 | 1 | 227 | 0.16 | -0.20 | -0.77 | 0.27 | -0.05 | -0.61 | 0.75 |

2 | 4 | 2 | 147 | 0.19 | -0.02 | -0.82 | 0.25 | -0.01 | -0.61 | -0.10 |

2 | 4 | 3 | 131 | 0.19 | -0.28 | -0.82 | 0.18 | -0.04 | 0.55 | 0.43 |

2 | 4 | 4 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 4 | 5 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 4 | 6 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 4 | 7 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 4 | 8 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 4 | 9 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 4 | 10 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 4 | 11 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 4 | 12 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 4 | 13 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 4 | 14 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 4 | 15 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 5 | 1 | 16 | 0.08 | -1.06 | 0.92 | -0.54 | -0.72 | -0.61 | -0.65 |

2 | 5 | 2 | 19 | 0.17 | -0.27 | 0.66 | -0.78 | -0.74 | 0.55 | -0.08 |

2 | 5 | 3 | 22 | 0.08 | -0.25 | 0.92 | -1.16 | -1.02 | -0.61 | -0.02 |

2 | 5 | 4 | 10 | 0.08 | -0.21 | 0.09 | -0.66 | -0.64 | -0.61 | -1.10 |

2 | 5 | 5 | 9 | 0.08 | -0.72 | 0.09 | -0.66 | -0.76 | -0.61 | -0.71 |

2 | 5 | 6 | 13 | 0.11 | -0.79 | 0.92 | -0.91 | -0.78 | -0.61 | -1.27 |

2 | 5 | 7 | 16 | 0.12 | -0.09 | 0.92 | -0.56 | -0.52 | -0.61 | -0.88 |

2 | 5 | 8 | 17 | 0.11 | -0.19 | 0.92 | -0.32 | -0.58 | -0.61 | -0.02 |

2 | 5 | 9 | 21 | 0.06 | -0.85 | 0.92 | -0.80 | -0.72 | -0.61 | 0.06 |

2 | 5 | 10 | 6 | 0.07 | -0.08 | 0.92 | -0.89 | -0.72 | -0.61 | -1.57 |

2 | 5 | 11 | 20 | 0.07 | 0.10 | 0.09 | -0.64 | -0.72 | -0.61 | -0.05 |

2 | 5 | 12 | 12 | 0.15 | -0.87 | 0.57 | -0.48 | -0.75 | 0.55 | -0.55 |

2 | 5 | 13 | 20 | 0.09 | -0.57 | 0.09 | -1.10 | -1.01 | -0.61 | -0.03 |

2 | 5 | 14 | 26 | 0.09 | 0.40 | 0.92 | -0.60 | -0.72 | -0.61 | -0.14 |

2 | 5 | 15 | 14 | 0.2 | -0.27 | 0.56 | -0.77 | -0.63 | 0.55 | -1.18 |

2 | 6 | 1 | 5 | 0.01 | 0.51 | -0.78 | 1.73 | 1.63 | 0.55 | -1.30 |

2 | 6 | 2 | 5 | 0.01 | 0.19 | -0.78 | 1.73 | 1.63 | 0.55 | -1.30 |

2 | 6 | 3 | 5 | 0.14 | 1.24 | -0.78 | 4.82 | -0.09 | -0.38 | 0.49 |

2 | 6 | 4 | 7 | 0.03 | 0.50 | -0.78 | 1.73 | 1.63 | 0.55 | -0.68 |

2 | 6 | 5 | 3 | 0.17 | 1.30 | -0.78 | 3.06 | 1.63 | -0.61 | -0.03 |

2 | 6 | 6 | 20 | 0.14 | 0.55 | 0.38 | 1.82 | 1.63 | 0.55 | -1.06 |

2 | 6 | 7 | 51 | 0.2 | 1.29 | -0.54 | 3.06 | 3.19 | 0.82 | -0.87 |

2 | 6 | 8 | 11 | 0.2 | 0.03 | -0.78 | 3.16 | 0.06 | -0.40 | -0.36 |

2 | 6 | 9 | 32 | 0.08 | 1.19 | -0.78 | 3.06 | 3.19 | -0.54 | 0.10 |

2 | 6 | 10 | 6 | 0.01 | 0.12 | -0.78 | 1.73 | 1.63 | 0.55 | -0.84 |

2 | 6 | 11 | 5 | 0.01 | 0.19 | -0.78 | 1.73 | 1.63 | 0.55 | -0.62 |

2 | 6 | 12 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 6 | 13 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 6 | 14 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 6 | 15 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 7 | 1 | 45 | 0.15 | 0.62 | -0.83 | 0.75 | 1.63 | 0.55 | 0.43 |

2 | 7 | 2 | 54 | 0.17 | 1.18 | 0.01 | 0.76 | 1.51 | -0.61 | 0.54 |

2 | 7 | 3 | 149 | 0.13 | 0.65 | -0.91 | 0.85 | 1.63 | -0.61 | 0.53 |

2 | 7 | 4 | 38 | 0.19 | 0.66 | -0.81 | 0.65 | 1.63 | -0.46 | -0.69 |

2 | 7 | 5 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 7 | 6 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 7 | 7 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 7 | 8 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 7 | 9 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 7 | 10 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 7 | 11 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 7 | 12 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 7 | 13 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 7 | 14 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 7 | 15 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 8 | 1 | 37 | 0.2 | -0.53 | 0.72 | 0.87 | 0.00 | 0.55 | -1.02 |

2 | 8 | 2 | 57 | 0.16 | -0.37 | 0.87 | 0.57 | 0.04 | 0.55 | -0.15 |

2 | 8 | 3 | 64 | 0.12 | -0.27 | 0.92 | 0.47 | 0.02 | -0.61 | -0.10 |

2 | 8 | 4 | 61 | 0.15 | -0.61 | 0.90 | 0.47 | -0.09 | -0.61 | -0.83 |

2 | 8 | 5 | 13 | 0.19 | -0.11 | 0.54 | 3.06 | 0.06 | -0.26 | -0.81 |

2 | 8 | 6 | 26 | 0.17 | -0.19 | -0.08 | 0.76 | 0.06 | -0.53 | -0.95 |

2 | 8 | 7 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 8 | 8 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 8 | 9 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 8 | 10 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 8 | 11 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 8 | 12 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 8 | 13 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 8 | 14 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 8 | 15 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 9 | 1 | 29 | 0.14 | -1.33 | -0.66 | -0.07 | -0.70 | 0.55 | -0.54 |

2 | 9 | 2 | 37 | 0.18 | -0.85 | -0.82 | -0.78 | -0.68 | 0.55 | -0.93 |

2 | 9 | 3 | 258 | 0.2 | -1.08 | -0.79 | -0.58 | -0.69 | -0.61 | -0.76 |

2 | 9 | 4 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 9 | 5 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 9 | 6 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 9 | 7 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 9 | 8 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 9 | 9 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 9 | 10 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 9 | 11 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 9 | 12 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 9 | 13 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 9 | 14 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 9 | 15 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 10 | 1 | 65 | 0.2 | -0.62 | 0.38 | 0.07 | -0.62 | -0.61 | 0.72 |

2 | 10 | 2 | 75 | 0.16 | -0.71 | 0.99 | -0.98 | -0.94 | -0.61 | 0.92 |

2 | 10 | 3 | 45 | 0.15 | -0.69 | 0.09 | -0.52 | -0.72 | 0.55 | 0.62 |

2 | 10 | 4 | 92 | 0.15 | -0.70 | 0.09 | -0.90 | -0.82 | -0.61 | 0.74 |

2 | 10 | 5 | 67 | 0.11 | -0.02 | 0.89 | -0.66 | -0.68 | -0.61 | 0.76 |

2 | 10 | 6 | 57 | 0.18 | -0.49 | 0.95 | -0.53 | -0.68 | 0.55 | 0.76 |

2 | 10 | 7 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 10 | 8 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 10 | 9 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 10 | 10 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 10 | 11 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 10 | 12 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 10 | 13 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 10 | 14 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 10 | 15 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 11 | 1 | 19 | 0.1 | 0.97 | 1.14 | 0.82 | 1.63 | -0.61 | 1.27 |

2 | 11 | 2 | 32 | 0.12 | 1.51 | 0.79 | 0.68 | 1.63 | 0.55 | 0.62 |

2 | 11 | 3 | 21 | 0.13 | 0.82 | 0.98 | 0.63 | 1.63 | 0.55 | 1.25 |

2 | 11 | 4 | 6 | 0.14 | 1.39 | 0.92 | 0.20 | 1.63 | 0.16 | -1.08 |

2 | 11 | 5 | 15 | 0.38 | 1.44 | 2.67 | 1.16 | 1.21 | 0.01 | -0.46 |

2 | 11 | 6 | 22 | 0.13 | 1.33 | 0.80 | 0.56 | 1.63 | 0.55 | -0.24 |

2 | 11 | 7 | 20 | 0.15 | 1.22 | 2.67 | 1.05 | 1.63 | -0.61 | 0.72 |

2 | 11 | 8 | 18 | 0.17 | 1.31 | 2.67 | 0.79 | 1.63 | 0.94 | 1.12 |

2 | 11 | 9 | 9 | 0.11 | 0.40 | 0.97 | 0.71 | 1.63 | 0.55 | -0.74 |

2 | 11 | 10 | 5 | 0.29 | 2.72 | 0.92 | 0.59 | 1.94 | 0.32 | 0.13 |

2 | 11 | 11 | 18 | 0.12 | 0.65 | 0.92 | 1.07 | 1.63 | -0.61 | 0.04 |

2 | 11 | 12 | 15 | 0.07 | 0.62 | 0.95 | 0.77 | 1.63 | 0.55 | 0.37 |

2 | 11 | 13 | 11 | 0.07 | 0.72 | 0.92 | 1.74 | 1.63 | 0.55 | -0.66 |

2 | 11 | 14 | 43 | 0.09 | 1.51 | 0.92 | 0.77 | 1.63 | -0.61 | 0.38 |

2 | 11 | 15 | 34 | 0.09 | 1.21 | 0.92 | 0.09 | 1.63 | 0.55 | 0.44 |

2 | 12 | 1 | 140 | 0.18 | -0.92 | -0.90 | -0.76 | -0.80 | 0.55 | 0.59 |

2 | 12 | 2 | 364 | 0.14 | -0.61 | -0.88 | -0.83 | -0.74 | -0.61 | 0.41 |

2 | 12 | 3 | 413 | 0.17 | -1.32 | -0.94 | -0.83 | -0.79 | -0.61 | 0.67 |

2 | 12 | 4 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 12 | 5 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 12 | 6 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 12 | 7 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 12 | 8 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 12 | 9 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 12 | 10 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 12 | 11 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 12 | 12 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 12 | 13 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 12 | 14 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 12 | 15 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 13 | 1 | 11 | 0.16 | 0.99 | 0.84 | -0.21 | -0.01 | -0.61 | -1.29 |

2 | 13 | 2 | 14 | 0.16 | 1.34 | 0.50 | 0.08 | 1.63 | -0.61 | -2.10 |

2 | 13 | 3 | 14 | 0.12 | 0.57 | 0.39 | -0.54 | -0.05 | 0.55 | -2.24 |

2 | 13 | 4 | 13 | 0.14 | 0.80 | 0.92 | -0.36 | -0.06 | 0.55 | -1.35 |

2 | 13 | 5 | 11 | 0.23 | 2.40 | 0.92 | 0.63 | -0.01 | 0.23 | -1.60 |

2 | 13 | 6 | 15 | 0.24 | 1.09 | 0.13 | -0.20 | 1.63 | 0.55 | -1.93 |

2 | 13 | 7 | 18 | 0.14 | 0.49 | 0.04 | -0.35 | -0.02 | 0.55 | -1.37 |

2 | 13 | 8 | 15 | 0.16 | 0.55 | -0.20 | 0.27 | 0.06 | -0.61 | -1.32 |

2 | 13 | 9 | 39 | 0.12 | 0.45 | 0.58 | -0.59 | 0.06 | -0.61 | -2.20 |

2 | 13 | 10 | 11 | 0.14 | 2.58 | -0.31 | 0.40 | 0.06 | -0.61 | -2.20 |

2 | 13 | 11 | 15 | 0.11 | 2.68 | 0.92 | 0.52 | 0.06 | -0.61 | -2.16 |

2 | 13 | 12 | 18 | 0.14 | 1.30 | 0.92 | -0.36 | -0.28 | 0.55 | -2.31 |

2 | 13 | 13 | 9 | 0.17 | 1.24 | 0.55 | -0.75 | -0.37 | -0.61 | -2.35 |

2 | 13 | 14 | 20 | 0.13 | 0.87 | -0.91 | 0.06 | 1.63 | -0.56 | -2.12 |

2 | 13 | 15 | 6 | 0.14 | 0.69 | -0.78 | -0.37 | 0.06 | 0.36 | -2.13 |

2 | 14 | 1 | 3 | 0.04 | 1.52 | 0.92 | 3.06 | 3.19 | 0.55 | 0.08 |

2 | 14 | 2 | 6 | 0.29 | 2.14 | 0.92 | 3.25 | 2.15 | 2.88 | -0.39 |

2 | 14 | 3 | 2 | 0.06 | 2.26 | 0.92 | 8.30 | 1.63 | 0.55 | 0.27 |

2 | 14 | 4 | 3 | 0.04 | 2.01 | 2.67 | 3.06 | 1.63 | 0.55 | 1.18 |

2 | 14 | 5 | 7 | 0.05 | 2.13 | 0.80 | 3.06 | 3.19 | 0.55 | -0.62 |

2 | 14 | 6 | 10 | 0.05 | 1.53 | 0.92 | 3.06 | 3.19 | -0.61 | 0.27 |

2 | 14 | 7 | 8 | 0.18 | 3.51 | 0.18 | 3.42 | 1.63 | -0.61 | 0.77 |

2 | 14 | 8 | 19 | 0.12 | 1.99 | 2.67 | 3.06 | 3.11 | -0.43 | 0.06 |

2 | 14 | 9 | 5 | 0.02 | 1.52 | 0.92 | 3.06 | 3.19 | 0.55 | -1.30 |

2 | 14 | 10 | 6 | 0.24 | 1.92 | 0.92 | 4.58 | -0.07 | -0.42 | 0.49 |

2 | 14 | 11 | 4 | 0.02 | 1.24 | 0.92 | 3.06 | 3.19 | 0.55 | -0.84 |

2 | 14 | 12 | 3 | 0.02 | 5.26 | 0.92 | 4.01 | 4.76 | 2.88 | 0.46 |

2 | 14 | 13 | 14 | 0.22 | 1.24 | 0.92 | 3.25 | 1.40 | 0.47 | 0.25 |

2 | 14 | 14 | 2 | 0.04 | 2.89 | 0.92 | 3.06 | 1.63 | -0.61 | -1.37 |

2 | 14 | 15 | 5 | 0.02 | 1.57 | 0.92 | 3.06 | 3.19 | -0.61 | -0.30 |

2 | 15 | 1 | 23 | 0.08 | -0.20 | 0.92 | -1.07 | -0.80 | -0.61 | -2.28 |

2 | 15 | 2 | 100 | 0.15 | -0.84 | -0.94 | -0.99 | -0.77 | -0.57 | -2.21 |

2 | 15 | 3 | 58 | 0.15 | -0.21 | 0.09 | -0.86 | -0.58 | -0.59 | -2.16 |

2 | 15 | 4 | 74 | 0.16 | 0.02 | -0.83 | -0.55 | -0.14 | -0.61 | -2.11 |

2 | 15 | 5 | 41 | 0.19 | 0.05 | -0.72 | -0.62 | -0.28 | 0.55 | -2.08 |

2 | 15 | 6 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 15 | 7 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 15 | 8 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 15 | 9 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 15 | 10 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 15 | 11 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 15 | 12 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 15 | 13 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 15 | 14 | 0 | NA | NA | NA | NA | NA | NA | NA |

2 | 15 | 15 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 1 | 1 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 1 | 2 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 1 | 3 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 1 | 4 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 1 | 5 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 1 | 6 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 1 | 7 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 1 | 8 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 1 | 9 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 1 | 10 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 1 | 11 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 1 | 12 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 1 | 13 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 1 | 14 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 1 | 15 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 2 | 1 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 2 | 2 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 2 | 3 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 2 | 4 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 2 | 5 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 2 | 6 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 2 | 7 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 2 | 8 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 2 | 9 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 2 | 10 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 2 | 11 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 2 | 12 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 2 | 13 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 2 | 14 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 2 | 15 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 3 | 1 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 3 | 2 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 3 | 3 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 3 | 4 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 3 | 5 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 3 | 6 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 3 | 7 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 3 | 8 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 3 | 9 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 3 | 10 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 3 | 11 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 3 | 12 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 3 | 13 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 3 | 14 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 3 | 15 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 4 | 1 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 4 | 2 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 4 | 3 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 4 | 4 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 4 | 5 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 4 | 6 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 4 | 7 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 4 | 8 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 4 | 9 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 4 | 10 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 4 | 11 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 4 | 12 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 4 | 13 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 4 | 14 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 4 | 15 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 5 | 1 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 5 | 2 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 5 | 3 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 5 | 4 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 5 | 5 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 5 | 6 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 5 | 7 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 5 | 8 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 5 | 9 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 5 | 10 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 5 | 11 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 5 | 12 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 5 | 13 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 5 | 14 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 5 | 15 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 6 | 1 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 6 | 2 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 6 | 3 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 6 | 4 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 6 | 5 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 6 | 6 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 6 | 7 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 6 | 8 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 6 | 9 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 6 | 10 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 6 | 11 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 6 | 12 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 6 | 13 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 6 | 14 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 6 | 15 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 7 | 1 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 7 | 2 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 7 | 3 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 7 | 4 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 7 | 5 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 7 | 6 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 7 | 7 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 7 | 8 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 7 | 9 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 7 | 10 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 7 | 11 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 7 | 12 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 7 | 13 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 7 | 14 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 7 | 15 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 8 | 1 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 8 | 2 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 8 | 3 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 8 | 4 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 8 | 5 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 8 | 6 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 8 | 7 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 8 | 8 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 8 | 9 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 8 | 10 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 8 | 11 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 8 | 12 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 8 | 13 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 8 | 14 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 8 | 15 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 9 | 1 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 9 | 2 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 9 | 3 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 9 | 4 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 9 | 5 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 9 | 6 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 9 | 7 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 9 | 8 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 9 | 9 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 9 | 10 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 9 | 11 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 9 | 12 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 9 | 13 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 9 | 14 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 9 | 15 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 10 | 1 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 10 | 2 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 10 | 3 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 10 | 4 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 10 | 5 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 10 | 6 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 10 | 7 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 10 | 8 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 10 | 9 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 10 | 10 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 10 | 11 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 10 | 12 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 10 | 13 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 10 | 14 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 10 | 15 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 11 | 1 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 11 | 2 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 11 | 3 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 11 | 4 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 11 | 5 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 11 | 6 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 11 | 7 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 11 | 8 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 11 | 9 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 11 | 10 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 11 | 11 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 11 | 12 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 11 | 13 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 11 | 14 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 11 | 15 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 12 | 1 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 12 | 2 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 12 | 3 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 12 | 4 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 12 | 5 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 12 | 6 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 12 | 7 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 12 | 8 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 12 | 9 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 12 | 10 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 12 | 11 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 12 | 12 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 12 | 13 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 12 | 14 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 12 | 15 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 13 | 1 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 13 | 2 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 13 | 3 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 13 | 4 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 13 | 5 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 13 | 6 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 13 | 7 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 13 | 8 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 13 | 9 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 13 | 10 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 13 | 11 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 13 | 12 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 13 | 13 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 13 | 14 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 13 | 15 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 14 | 1 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 14 | 2 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 14 | 3 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 14 | 4 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 14 | 5 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 14 | 6 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 14 | 7 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 14 | 8 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 14 | 9 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 14 | 10 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 14 | 11 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 14 | 12 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 14 | 13 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 14 | 14 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 14 | 15 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 15 | 1 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 15 | 2 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 15 | 3 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 15 | 4 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 15 | 5 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 15 | 6 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 15 | 7 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 15 | 8 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 15 | 9 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 15 | 10 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 15 | 11 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 15 | 12 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 15 | 13 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 15 | 14 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 15 | 15 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 16 | 1 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 16 | 2 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 16 | 3 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 16 | 4 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 16 | 5 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 16 | 6 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 16 | 7 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 16 | 8 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 16 | 9 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 16 | 10 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 16 | 11 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 16 | 12 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 16 | 13 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 16 | 14 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 16 | 15 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 17 | 1 | 3 | 0.05 | 0.96 | 0.09 | 1.73 | 1.63 | 2.88 | -0.92 |

3 | 17 | 2 | 3 | 0.03 | 0.76 | -0.78 | 1.73 | 1.63 | 2.88 | -0.69 |

3 | 17 | 3 | 2 | 0.02 | 0.83 | -0.78 | 1.73 | 1.63 | 2.88 | -1.30 |

3 | 17 | 4 | 2 | 0.07 | 1.76 | -0.35 | 0.87 | 0.06 | 2.88 | -1.08 |

3 | 17 | 5 | 2 | 0.06 | 2.32 | -0.78 | -0.06 | 1.63 | 2.88 | -1.37 |

3 | 17 | 6 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 17 | 7 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 17 | 8 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 17 | 9 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 17 | 10 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 17 | 11 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 17 | 12 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 17 | 13 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 17 | 14 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 17 | 15 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 18 | 1 | 7 | 0.09 | 1.31 | 0.09 | -0.08 | 0.06 | 2.88 | 0.85 |

3 | 18 | 2 | 5 | 0.08 | 1.09 | 0.92 | -0.73 | -0.72 | 2.88 | 0.83 |

3 | 18 | 3 | 7 | 0.11 | 1.31 | 0.09 | -0.25 | -0.72 | 2.88 | 0.73 |

3 | 18 | 4 | 0 | NA | NA | NA | NA | NA | NA | NA |

3 | 18 | 5 | 0 | NA | NA | NA | NA | NA | NA | NA |

Let’s check the compression summary to check how many cells in each level are above the quantization error threshold.

segmentLevel | noOfCells | noOfCellsBelowQuantizationError | percentOfCellsBelowQuantizationErrorThreshold |
---|---|---|---|

1 | 15 | 0 | 0 |

2 | 134 | 118 | 0.88 |

3 | 50 | 50 | 1 |

As it can be seen from the compression summary table above, the Quantization Error for most of the cells in level 3 fall below the defined quantization threshold. Hence, we were succesfully able to compress `89%`

of the data.

```
hvtHmap(hvt.results3,
trainComputers,
child.level = 3,
hmap.cols = "Quant.Error",
line.width = c(1.2,0.8,0.4),
color.vec = c("#141B41","#6369D1","#D8D2E1"),
palette.color = 6,
show.points = T,
centroid.size = 1,
quant.error.hmap = 0.2,
nclust.hmap = 15)
```

Now we will try to get more insights from the cells by overlaying heatmap for variable `price`

at different levels.

Let’s do it for level one.

In the plot below, a heatmap for the variable `price`

is overlayed on a level one tesselation plot. We calculate the mean price for each cell and represent it as a heatmap.

The heatmap for the `price`

variable for different cells at level 1 can be seen in the plot below.

```
hvtHmap(hvt.results,
trainComputers,
child.level = 1,
hmap.cols = "price",
line.width = c(0.8),
color.vec = c("#141B41"),
palette.color = 6,
show.points = T,
centroid.size = 3,
quant.error.hmap = 0.2,
nclust.hmap = 15)
```

Now we will go one level deeper and overlay heatmap for `price`

at level 2. This should give us better insight about the price distribution for different cells.

In the plot below, we have overlayed heatmap for the variable `price`

at level 2.

```
hvtHmap(hvt.results2,
trainComputers,
child.level = 2,
hmap.cols = "price",
line.width = c(0.8,0.2),
color.vec = c("#141B41","#0582CA"),
palette.color = 6,
show.points = T,
centroid.size = 2,
quant.error.hmap = 0.2,
nclust.hmap = 15)
```