I have 4 sets of numeric continuous data that I used for regression analysis.
Now, I performed ROC analysis using "Fit Y by X" option of "JMP Pro" for each set of data which generated me 4 seperate AUC curves and graphs of ROC in the "JMP Pro" program.
I obtained these curves using the following steps:
My question is, how can I generate all of these graphs in one plot?
What I mean, when I did generated plots, it became like the two in the above of this attached picture. I want something like in the bottom of this picture
For example, the steps required in the software?
Related
I'm currently working on my bachelor project and I'm using the PointNet deep neural network.
My project group and I have created a dataset of point clouds(an unsorted list of x amount of 3d coordinates) and segmentation files, but we can't train PointNet to predict segmentation with the dataset.
Each segmentation file is a list containing the same amount of rows, as points in the corresponding point cloud, and each row is either a 1 or a 2, depending on the corresponding point belonging to segment 1 or 2.
When PointNet predicts it outputs a list of x elements, where each element is the segment that PointNet predicts the corresponding point belongs to.
When we run the benchmark dataset from the original PointNet implementation, the system runs and can predict segmentation, so we know that the error is in the dataset somewhere, even though we have tried our best to have our dataset look like the original benchmark dataset.
The implemented PointNet uses pytorch conv2d, maxpool2d and linear transformation. For calculating the loss, both the nn.functional.nll_loss and the nn.NLLLos functions have been used. When using the nn.NLLLos the weight parameter was set to a tensor of [1,100] to combat potential imbalance of the data.
These are the thing we have tried:
We have tried downsampling the point clouds i.e remove points using voxel downsampling
We have tried downscaling and normalize all values so they are between 0 and 1, using this formula (data - np.min(data)) / (np.max(data) - np.min(data))
We have tried running an euclidean clustering function on the data, to have each scanned object for it self
We have tried replicating another dataset, which was created using the same raw data, which we know have worked before
In the attached link, images of the datafiles with a description can be found.
Cheers everyone
After following some tutorials on LSTM networks, I've decided to put my knowledge in practice by training a LSTM model on my own dataset.
Here is a view of my data:
As you can observe, I have same number of samples and labels.
Let's say that I have 10 samples and 10 labels for those samples and I want to split those samples in 2 timesteps.
After spliting I would have 5 samples, each having 2 timesteps, but I would still have 10 labels.
Am I right?
How you guys deal with this problem?
If I'm trying to feed the data in this form, I will get a "Data cardinality is ambiguous" exception.
In an LSTM, every input sequence has one one label (in the case of simple classification, at least). So in your case you would have your data be two samples of the position, and then a single label.
I'm not a dev, I'm doing this for a school project. I'm trying to put the following dataset into a surface plot in windows gnuplot. qt type terminal, if that's important.
https://files.catbox.moe/nbc6l1.json
As you can see, it's a huge set of data. Pulled directly from an image and into a csv file, which I converted to json.
When I type in "splot 'C:\Users\tyler\ESRP Data\sampleOutput.json'", this is what I get.
As you can see, there's only a single line, when there should be something approaching an intensity chart in a 3 dimensional space. Is it a problem with the data? Do I need a specific command to do this?
It would help if you attached an example of your image data to the question, and also if you provided a link to a plot similar to the one you are trying to create. There are many different styles one might use to represent a surface. I will attempt to guess at a possible solution.
Input image (scribbled in GIMP and saved as a png image):
Gnuplot surface plot:
set border -1
unset tics
# surface represented by colored lines in 3D
# down-sample by 4x in each dimension to get an interpretable surface
set palette defined (0 "blue", 1 "white")
splot 'scribble.png' binary filetype=png every 4:4:4 using 1:2:3:3 with lines lc palette
I'm trying to develop a model to recognize new gestures with the Myo Armband. (It's an armband that possesses 8 electrical sensors and can recognize 5 hand gestures). I'd like to record the sensors' raw data for a new gesture and feed it to a model so it can recognize it.
I'm new to machine/deep learning and I'm using CNTK. I'm wondering what would be the best way to do it.
I'm struggling to understand how to create the trainer. The input data looks like something like that I'm thinking about using 20 sets of these 8 values (they're between -127 and 127). So one label is the output of 20 sets of values.
I don't really know how to do that, I've seen tutorials where images are linked with their label but it's not the same idea. And even after the training is done, how can I avoid the model to recognize this one gesture whatever I do since it's the only one it's been trained for.
An easy way to get you started would be to create 161 columns (8 columns for each of the 20 time steps + the designated label). You would rearrange the columns like
emg1_t01, emg2_t01, emg3_t01, ..., emg8_t20, gesture_id
This will give you the right 2D format to use different algorithms in sklearn as well as a feed forward neural network in CNTK. You would use the first 160 columns to predict the 161th one.
Once you have that working you can model your data to better represent the natural time series order it contains. You would move away from a 2D shape and instead create a 3D array to represent your data.
The first axis shows the number of samples
The second axis shows the number of time steps (20)
The thirst axis shows the number of sensors (8)
With this shape you're all set to use a 1D convolutional model (CNN) in CNTK that traverses the time axis to learn local patterns from one step to the next.
You might also want to look into RNNs which are often used to work with time series data. However, RNNs are sometimes hard to train and a recent paper suggests that CNNs should be the natural starting point to work with sequence data.
We have to do multiple regression for a college project. We have all the data, but we don't know, how to plot it and howenter image description here to draw a regression line.
Our current programming
If you are new to R, try the visreg pacakge. Suppose you you have 3 explanatory variables in your model:
library(visreg)
lm.result <- lm(povred~lnenp+maj+pr)
Since you have 3 variables, visreg would produce 3 plots. In each plot the other two variables are held at mean. So you first want to define a 2x2 space for plots:
par(mfrow=c(2,2))
Then just ask visreg to plot your lm result:
visreg(lm.result)
This is how the plots should look like