I'm dynamically casting my tensor into different types based on what the input type string is.
How do I get tf.float64 from a string 'float64'? I tried tf.getattr('float64'), but tf is a module that has no getattr method.
I'm hacking it by creating a lookup for now, but I'm sure there's a cleaner way.
Solved it myself:
tf.dtypes.as_dtype('float32')
Related
This code is a basic input/output function in Kotlin. But I am facing an issue with syntax even though it is right.
I figured out the mistake here. first of i wanted to take an input an integer type and also a string type which must be provided by the user. so i tried to use scanner instead we could have used val a: Int =readLine()!!.toInt() for integer type. and val b:string=readLine()!! for string type.
I am trying to use the Accord.net library to build test method of several of the machine learning algorithms that library supports.
One of the issues I have run into is that when I am trying to codify my string data, the Codification class does not seem capable of dealing with any datatable columns that are not strings, despite the documentation saying otherwise.
Codification codebook = new Codification(fulldata, AllAttributeNames);
I call that line where fulldata is a datatable, and I have tried including columns of both Int32 type and Double type, and the Codification class has thrown an error saying it is unable to convert them to type String.
"System.InvalidCastException: 'Unable to cast object of type 'System.Double' to type 'System.String'.'"
EDIT: It turns out this error is because the Codification system can only handle alternate data types if it is encoding the entire table. I suppose I can see the logic here, although I would prefer a better error, or that the method was a little smarter.
I now have another issue that has cropped up related to this. After changing my code to this:
Codification codebook = new Codification(fulldata);
I then learning.Learn(inputs, outputs) my algorithm and want to use the newly trained algorithm. So the next step would be to take a bunch of test data, make sure it matches the codebooks encoding, and send it through the algorithm. Unfortunately, when I try and use the
int[][] testinput = codebook.Transform(testData, inputColumnNameArray);
It blows up claiming it could not find a mapping to transform. It does this in reference to an Integer column that the codebook correctly did not map to new values. So now it seems this Transform method is not capable of handling non-string columns, and I have not found an overload of it that can, even though the documentation indicates it should be able to handle this.
Does anyone know how to get around this issue without manually building the entire int[][] testinput array one value at a time?
Turns out I was able to answer my own question eventually.
The Codification class has two methods of using it as near as I can tell. The constructor that takes a list of column names, as well as the Transform methods both lack intelligence in dealing with non-string data types, perhaps these methods are going away in the future.
The constructor that just takes a datatable by itself, as well as the Apply method, are both capable of handling data types other than strings. Once I switched to using these two methods my errors went away.
Codification codebook = new Codification(fulldata);
int[][] testinput = codebook.Apply(testData, inputColumnNameArray);
The confusion for me lay in all the example code seemingly randomly using these two methods, but using the Apply method only when processing the training data, and using the Transform method when encoding test data.
I am not sure why they chose to do this in the documentation example code, but it definitely took me a long time to figure out what was going on enough to stop having this particular issue.
I'm trying to add a new discrete distribution to PyMC3 (a Wallenius non-central hypergeometric) by wrapping Agner Fogs c++ version of it (https://www.agner.org/random/).
I have successfully put the relevant functions in a c++ extension and added broadcasting so that it behaves as scipy's distributions. (For now broadcasting is done in Python. .. will later try the xtensor-python bindings for more performant vectorization in c++.)
I'm running into the following problem: when I instantiate an RV of the new distribution in a model context, I'm getting a "TypeError: an integer is required (got type FreeRV)" from where "value" is passed to the logp() function of the new distribution.
I understand that PyMC3 might need to connect RVs to the functions, but I find no way to cast them into something my new functions can work with.
Any hints on how to resolve this or general info on adding new distributions to PyMC3 or the internal workings of distributions would be extremely helpful.
Thanks in advance!
Jan
EDIT: I noticed that FreeRV inherits from theanos TensorVariable, so I tried calling .eval(). This leads to another error along the lines that no input is connected. (I don't have the exact error message right now).
One thing which puzzles me is why logp is called at instantiation of the variable when setting up the model ...
I am trying to process a dataset with JSON data. However, the data have been written on a file without being parsed. That means that a python dictionary is written in the file as a string instead of a JSON object as a string.
I've found a module (AST) that will do the job to convert the string to a dictionary again using the ast.literal_eval function.
However, I am getting a very strange error in some of the instances:
The code reads from a text file and apply the following to each line:
ast.literal_eval(line.rstrip())
It seems some of the characters are not ok with the AST module.
Need to recall as well that this is not happening with all the dataset, just with some instances.
Any ideas?
Many thanks in advance.
Try exploring the json package. It is cleaner and more standard way of converting strings to dictionary
json.loads(inputStr) // Converts string -> dict
json.dumps(inputJson) // Converts dict -> string
Hope this helps. Cheers!
I'm trying register presenters with Windsor using the convention based method but trying to do this in VB.NET, but the problem is it does not want to compile this statement:
Dim type = GetType(AbstractPresenter(Of))
I am getting : Too few type arguments to AbstractPresenter(Of TView, TPresenter)
Which I don't understand because this is a valid statement according to question. Also showing valid in other C# to VB.NET converters when converting typeof(AbstractPresenter<>).
Any ideas?
There are two type arguments, and you need to specify this, just as you would do for multi-dimensional arrays:
Dim type = GetType(AbstractPresenter(Of ,))
Looks weird, but now the compiler knows that AbstractPresenter expects two type arguments.
By the way, C# has the same requirement. So the above would be written as:
var type = typeof(AbstractPresenter<,>);