I want to preform some kind of smoothing filtering with numpy or scipy(or any other python package) on my 2d data set(2d median filter for example) but I don't want the filtering kernel to cross some arbitrary line in my data set.
A Toy example for the data set will look something like:
Essentially I don't want the filter to mix between the positive and negative values across the black line in the toy example. In the general problem the data can contain multiple lines and the lines can have any orientation or length.
I would appreciate any solution or suggestions
Related
So I have been looking at XGBoost as a place to start with this, however I am not sure the best way to accomplish what I want.
My data is set up something like this
Where every value, whether it be input or output is numerical. The issue I'm facing is that I only have 3 input data points per several output data points.
I have seen that XGBoost has a multi-output regression method, however I am only really seeing it used to predict around 2 outputs per 1 input, whereas my data may have upwards of 50 output points needing to be predicted with only a handful of scalar input features.
I'd appreciate any ideas you may have.
For reference, I've been looking at mainly these two demos (they are the same idea just one is scikit and the other xgboost)
https://machinelearningmastery.com/multi-output-regression-models-with-python/
https://xgboost.readthedocs.io/en/stable/python/examples/multioutput_regression.html
I am trying to create a machine learning model to predict the position of each team, but I am having trouble organizing the data in a way the model can train off of it.
I want the pandas dataframe to look something like this
Where each tournament has team members constantly shifting teams.
And based on the inputted teammates, the model makes a prediction on the team's position. Anyone have any suggestions on how I can make a pandas dataframe like this that a model can use as trainnig data? I'm completely stumped. Thanks in advance!
Coming on to the question as to how to create this sheet, you can easily get the data and store in the format you described above. The trick is in how to use it as training data to your model. We need to convert it in numerical form to be able to be used as training data to any model. As we know that the max team size is 3 in most cases, we can divide the three names in three columns (keep the column blank, if there are less than 3 members in the team). Now we can either use Label encoding or One-hot encoding to convert the names to numbers. You should create a combined list of all three columns to fit a LabelEncoder and then use transform function individually on each column (since the names might be shared in these 3 columns). On label encoding, we can easily use tree based models. One-hot encoding might lead to curse of dimensionality as there will be many names, so I would prefer not to use it for an initial simple model.
I've got the following task: there are two outputs from DAQ, namely speed and the raw data acquired along with this speed. I'd like to use speed as a parameter to define certain number of bins, and fit the raw data which corresponds to the speed into the specific bin. I am not sure how to do this in LabVIEW - because when I check the histogram function, it seems that it only requires one input (1D array of values).
Many thanks, any help is much appreciated. Aileen
The Histogram VI takes an array of data and the number of bins you want, and determines the boundaries of the bins automatically. It sounds like that's the one you're looking at.
The General Histogram VI allows you to specify the bins yourself. If you can't find it, perhaps you only have the LabVIEW Base Package development system, as it's only present in the Full Development System and above.
If you don't have General Histogram and you need to create a histogram using your own bin boundaries, it wouldn't be too hard to create. Without writing the code for you, you could do something like:
Create a 1D array containing your bin boundaries in ascending order.
Use a For loop to index through the array of bin boundaries
In the loop, use (e.g.) >, <=, and And functions to get a Boolean array which contains True for each value in the data array that should be in the current bin
Use Boolean to (0,1) and Add Array Elements to count the number of True values.
If any of that's unclear, please edit your question with more details and perhaps an example of some input data and what you want the output to be.
This is an implementation of nekomatic's description.
The first SubVi just creates the 1D array containing your bin boundaries.
X_in and Y_in are the independent and dependent input datasets. Both have to be of equal length but must not be sorted. In the inner For loop it will be checked if X_in fits into the current bin. If so, X_in and the corresponding Y_in value are stored in a temporary arrays which are averaged afterwards.
Maybe it is not the most efficient code but at least it seems to be not slower than the General Histogram VI
I download the following graph-cut code:
https://github.com/shaibagon/GCMex
I compiled the mex files, and ran it for pre-defined image in the code (which is rgb image)
I wanna optimize the image segmentation results,
I have probability map of the image, which its dimension is (width,height, 5). Five probability distribution over the image dimension are stacked together. each relates to one the classes.
My problem is which parts of code should according to the probability image.
I want to define Data and Smoothing terms based on my application.
My question is:
1) Has someone refined the code according to the defining different energy function (I wanna change Unary and pair-wise formulation).
2) I have a stack of 3D images. I wanna define 6-neighborhood system, 4 neighbors in current slice and the other two from two adjacent slices. In which function and part of code can I do the refinements?
Thanks
I want to compute means with bootstrap confidence intervals for some subsets of a dataframe; the ultimate goal is to produce bar graphs of the means with bootstrap confidence intervals as the error bars. My data frame looks like this:
ATG12 Norm ATG5 Norm ATG7 Norm Cancer Stage
5.55 4.99 8.99 IIA
4.87 5.77 8.88 IIA
5.98 7.88 8.34 IIC
The subsets I'm interested in are every combination of Norm columns and cancer stage. I've managed to produce a table of means using:
df.groupby('Cancer Stage')['ATG12 Norm', 'ATG5 Norm', 'ATG7 Norm'].mean()
But I need to compute bootstrap confidence intervals to use as error bars for each of those means using the approach described here: http://www.randalolson.com/2012/08/06/statistical-analysis-made-easy-in-python/
It boils down to:
import scipy
import scikits.bootstraps as bootstraps
CI = bootstrap.ci(data=Series, statfunction=scipy.mean)
# CI[0] and CI[1] are your low and high confidence intervals
I tried to apply this method to each subset of data with a nested-loop script:
for i in data.groupby('Cancer Stage'):
for p in i.columns[1:3]: # PROBLEM!!
Series = i[p]
print p
print Series.mean()
ci = bootstrap.ci(data=Series, statfunction=scipy.mean)
Which produced an error message
AttributeError: 'tuple' object has no attribute called 'columns'
Not knowing what "tuples" are, I have some reading to do but I'm worried that my current approach of nested for loops will leave me with some kind of data structure I won't be able to easily plot from. I'm new to Pandas so I wouldn't be surprised to find there's a simpler, easier way to produce the data I'm trying to graph. Any and all help will be very much appreciated.
The way you iterate over the groupby-object is wrong! When you use groupby(), your data frame is sliced along the values in your groupby-column(s), together with these values as group names, forming a so-called "tuple":
(name, dataforgroup). The correct recipe for iterating over groupby-objects is
for name, group in data.groupby('Cancer Stage'):
print name
for p in group.columns[0:3]:
...
Please read more about the groupby-functionality of pandas here and go through the python-reference in order to understand what tuples are!
Grouping data frames and applying a function is essentially done in one statement, using the apply-functionality of pandas:
cols=data.columns[0:2]
for col in columns:
print data.groupby('Cancer Stage')[col].apply(lambda x:bootstrap.ci(data=x, statfunction=scipy.mean))
does everything you need in one line, and produces a (nicely plotable) series for you
EDIT:
I toyed around with a data frame object I created myself:
df = pd.DataFrame({'A':range(24), 'B':list('aabb') * 6, 'C':range(15,39)})
for col in ['A', 'C']:
print df.groupby('B')[col].apply(lambda x:bootstrap.ci(data=x.values))
yields two series that look like this:
B
a [6.58333333333, 14.3333333333]
b [8.5, 16.25]
B
a [21.5833333333, 29.3333333333]
b [23.4166666667, 31.25]