fn.py | Functional programming in Python: implementation of missing features to enjoy FP | Functional Programming library
kandi X-RAY | fn.py Summary
kandi X-RAY | fn.py Summary
Functional programming in Python: implementation of missing features to enjoy FP
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Top functions reviewed by kandi - BETA
- Cried a function .
- Iterates over the first exception and returns an exception .
- A utility function for unfolders .
- String representation of this object .
- Flip the function .
- Map fmap .
- Round the iterables .
- Applies a function to a function
- Pop a vector .
- Flatten a sequence of items
fn.py Key Features
fn.py Examples and Code Snippets
Lines of Code : 26License : Strong Copyleft (CC BY-SA 4.0)
Copycf_matrix_train.append(df)
df = df.append(cf_matrix_train)
df = pd.DataFrame(columns=['Key', 'TN', 'FP', 'FN', 'TP'])
max_f = list(range(1, 3))
max_d = list(range(10, 12))
index = 0
for ffrom functools import partial
for fn in ('tan','sin','cos'):
globals()[fn] = partial(lambda fname, arr: getattr(cp if isinstance(arr, cp.ndarray) else np, fname)(arr), fn)
def swLookAt(self,eyex,eyey,eyez,atx,aty,atz,upx,upy,upz):
# [...]
mylookat=np.matmul(M,T).transpose()
return mylookat
def swPerspective(self,fovy,aspect,near,far):
per=np.zeros((4,4))
!pip install numpy==1.19.5
!pip uninstall -y pycocotools
!pip install pycocotools --no-binary pycocotools
#!/bin/bash
cd /home/ec2-user/webapp
sudo rm -rf /var/lib/cloud/instance
sudo rm -rf /var/lib/cloud/instances
echo -n "" > .env
echo DB_HOST=${DatabaseInstance.Endpoint.Address} >> .env
eccd /home/ec2-user/webapp/release
typedef unsigned long long N;
N fn1(N a)
{
return a ^ (a >> 1);
}
N floor_ld(N x);
N fn1_inv(N b)
{
if (b<2) return b;
N p = (N)1 << (floor_ld(floor_ld(b)) + 1);
N y = b;
for (int i = 1; i <= p - 1;df1['NNA'] = pd.to_numeric(df1['NNA'], errors='coerce')
df1['AuM'] = pd.to_numeric(df1['AuM'], errors='coerce')
g = df1.groupby('FN')
df1['AuM_2'] = g['NNA'].cumsum() + g['AuM'].transform('first')
print (df1)
Date FN AuM NNA Audf = pd.read_excel('sample_file.xlsx')
#test all rows if previous row is only NaNs
m1 = df.shift(fill_value=0).isna().all(axis=1)
#test all rows if no NaNs
m2 = df.notna().all(axis=1)
#chain together and filter all next rows after first mfrom collections import Counter
files = Counter()
words = Counter()
for fn in list_of_files:
thisfile = set()
for word in open(fn).read().split():
words[word] += 1
thisfile.add( word )
for word in thisfile:
Community Discussions
Trending Discussions on fn.py
QUESTION
I try to create a new tensor based on a dictionary that maps 1 to 1 the values from a tensor to some other value (the example below is trivial on purpose), and i get the error "TypeError: Tensor is unhashable. Instead, use tensor.ref() as the key." - even though I do not use Tensors as keys in the dictionary, i convert them to int before:
...ANSWER
Answered 2021-May-31 at 03:02you are getting the error because when you type-casted using int(x) it was still a tensor. it was type tensorflow.python.framework.ops.EagerTensor . pls use numpy()(i.e tensor to numpy.int32).
so code change would be
QUESTION
I am trying to create a custom layer that calculates the forward kinematics for a robotic arm using 'DH parameters'. In my code, I am using the 6 joint angles as the input of the custom layer (Kinematics_Physics) and I am using tensorflow.map_fn to iteratively calculate the forward kinematics of each set of angles in the input. My goal is to set the 'DH parameters' as the trainable weights and train a model to optimize the 'DH parameters'. I am aware of the fact that this can be easily done using libraries like scipy.optimize or other optimization libraries, but I want to implement this in tensorflow so, I can add dense layers to learn the inverse kinematics of given custom layer. My code is as following:
ANSWER
Answered 2020-Oct-26 at 11:54Coming up with a solution was a bit of a journey.
The problem:map_fn expect a Tensor, not a symbolic representation of a Tensor.
The problem that caused your error is a bit tricky to explain. But basically, when you are creating your keras model, the batch_size is unknown. It is the standard behaviour so you can feed different batch size to your model. However, the map_fn function expect to know the shape of the Tensor you are feeding it to create the execution graph. Which is not possible given that the batch size of your Input Layer is unknown! Using map_fn inside a keras.Layer seems difficult to do.
The good thing is that we can skip the batch dimension by just referring to the part that interrest us : inp[...,0] gives us the first value of the last dimension of the inp Tensor without having to index on the batch_size.
The bad thing is that sometimes, we rely on the batch size, especially when we need some copies of the trainable weights for each input of the batch size. We can tell tensorflow to delay those calculation by using tf.shape
Some general comments about your code and the changes I made:
- you used a lot of
tf.Variableas class attributes. Unless you need to keep a state between different batches, this is not needed. It makes the code harder to read because you need to useassignintead of= - You used the
@tf.functiondecorator on each method class. You need it only if you use operations likeforloops that can only be eager executed and need to be converted before being executed in the Graph. - You overrided
__call__instead ofcall. Don't do that! The inherited__call__function oftf.keras.layers.Layerapplies internal tensorflow logic to get certain guaranties. Redefinecallinstead. - Don't use tf.Variable unless you need to track that variable. They are supposed to represent shared, persistent state your program manipulates. I suggest that you read the Variable Guide.
I was a bit liberal with the use of [...,] indexing.
QUESTION
I am trying to apply code from the question answer taken from: Use scipy.integrate.quad with Tensorflow to my problem. For more simplicity - I want to integrate three 2D arrays containing Legendre polynomials of the 1st, 2nd and 3rd degrees, respectively.
...ANSWER
Answered 2020-Aug-11 at 22:39Ok based on your update to the question, here's the code that hopefully works for you (tested). I've omitted 'i' and the integration goes from 0.0 ... 10.0 but you can modify to include that. The main idea is that if you want to use the tensor's value in a python function, you first need to convert it to its value by the numpy() method.
QUESTION
I am using MingW64 to compile Cython for Python 3.7. The task is entirely an excerise where I am calculating the Julia set for a grid of points. I am following along with a book, "High Performance Python", which is walking though how to use Cython. I have previously used the -O flag to set optimizations for DSP hardware and gotten significant improvements by setting this to be higher i.e. -O3. This is not the case for the Cython code though.
When doing the same thing with the Cython code it steadily produces slower results as the optimization is increased, which seems to make no sense. The timings I get are:
-O1 = 0.41s
-O2 = 0.46s
-O3 = 0.47s
-Ofast = 0.48s
-Os = 0.419s
Does anyone have an idea for why this would seem to be working in the opposite optimizations?
The cython code is in the file cythonfn.pyx
...ANSWER
Answered 2020-Aug-08 at 02:14It looks like an answer to this is at least partially explained here, gcc optimization flag -O3 makes code slower than -O2.
To summarize the results, using -O3 actually uses a different comparison/branch technique than -O2, so in this task where the comparison is occurring many times the choice of how it is done is important. Because of the predictability in the loop, it adds execution time by using this optimization.
This still does not solve the reason for going from -O1 to -O2 also, but this source is sufficient to describe the one case. Perhaps another knows the answer to this?
QUESTION
So here is a parser taken from fourFn.py:
ANSWER
Answered 2020-Jul-20 at 17:30It's likely not possible because it's Python being slow, not your interpreter. You're trying to compute 9 ** 387420489, which is a huge number (and Python's integers are virtually unbounded, so it'll just continue computing that integer until it runs out of memory). Since Python can only run one thread at a time, and integer exponentiation is implemented in C, pure Python code has no way of knowing that the interpreter is "stuck" computing something.
You could set up some other code to monitor the execution of this code and kill it if it doesn't terminate in n seconds, but that'd probably be overkill. BTW, I've been computing this number for 5 minutes already, and there's still no answer.
QUESTION
First I would like to say that I am just starting using pyparsing, and I need some help with the following. So here is the context:
I have a file with text lines. Each line can have one sequence or can be a set of sequences combined with the concurrent operator ||. The representation can either be (seq1)||(seq2)||... etc or simply seq1.
A sequence seq_ is a set of events, starting with a question followed by one or more answers, the sequence order is defined by the order of the answers on the line with the operator -> or => in between. The interpretation is that the system reading the line should execute the question and then check that the answers are identical to the ones defined in the line, in the defined order, hence the sequence. The previous definition of a line where several sequences running concurrently simply means that the question at the beginning of each sequence should be executed first and only then the system will check the answers for each question independently (concurrent execution and check).
The question format is as such qtn(elm,sub,num1[,num2,num3,...]) where what is between [] is optional and where elm and sub are names and num_ are numbers.
The answer is more complicated and can be one of the following:
ans(elma,acta,suba,num5[,num6,num7,...][elma.pr1=num8[,elma.pr2=num9]])[, meaning that somenum_are optional and the timeout too.ans(elma,acta,suba,num5[,num6,num7,...])[ where the|operator indicates aOR, meaning that one answerORthe other is enough to consider that the global answer is correct.ans(elma,acta,suba,num5[,num6,num7,...])[ where the&operator indicates aAND, meaning that both answers are required to consider that the global answer is correct.- and of course the answer can be a combination of elements with the
|and&operators mixed in with(). For example we could haveans(elma,acta,suba,num5[,num6,num7,...])[ or something more complex with or without(). There is no operator priority, only the()are indicating some order.
So my idea is to define the general syntax of the different big elements (and I am not sure it is the best way):
- the question would be:
...ANSWER
Answered 2020-Jun-29 at 17:19Your Regex and sample strings were good inputs for defining a simple parser, usually done a little more formally as a BNF, but these were sufficient. Here is a basic implementation of your simple ans format, you should be able to generalize from here what the question would look like:
QUESTION
I have some tensorflow model which I need to export in a saved model. Below is the simplified code of the model, which I am trying to export.
...ANSWER
Answered 2020-May-06 at 08:05You need to use a custom layer:
QUESTION
I try to write a learning case according to google's course which uses DNNRegressor to setup a neural nets(intro_to_neural_nets). But I get an error when executing the script:
...ANSWER
Answered 2019-Oct-30 at 20:30I don't think you're doing anything wrong here. I edited the example in the TensorFlow documentation using canned estimators and was unable to use any tf.keras.optimizer() to do training with multiple estimator.train(...) calls. Your code will likely run without specifying an optimizer, but I'm not sure what learning rate or optimizer is used in that case...
I just opened this as an issue on the TF github. See here for updates: https://github.com/tensorflow/tensorflow/issues/33358
If you want to get going immediately, you could downgrade your code to TF 1.x so that you can roughly match the version of the google machine learning crash course.
If you're feeling more ambitious, the TF team recommends starting to learn TensorFlow with Keras. From the documentation page about premade Estimators:
Note that in TensorFlow 2.0, the Keras API can accomplish many of these same tasks, and is believed to be an easier API to learn. If you are starting fresh, we would recommend you start with Keras. For more information about the available high level APIs in TensorFlow 2.0, see Standardizing on Keras.
Edit: An option to monitor training that would be low effort would be to use tensorboard. The changes to your code would be to:
Remove the looping.
Add model_dir parameter to find logs.
QUESTION
I'm loading a dataset with multiple input images. The input image paths should only be decoded at batch time, in order to handle a large dataset.
The data set is N image path inputs and M float outputs. The images for each input have different resolutions.
...ANSWER
Answered 2019-Sep-19 at 06:39This is a full code (save for defining json_parse_x_y and declaring AUTOTUNE) to achieve what you are attempting without using dict structures.
I tested that make_dataset works (see example below), so if you encounter an issue it should be due to a specification error regarding load_tensors.
QUESTION
I have a 3 dimensional matrix of shape (height, width, 4). In fact it is a bitmap with RGBA values per pixel. I would like to reduce each RGBA set to a set with two values, say [x,y].
see image at imgur com / Blr2EQC
I have tried using map_fn
...ANSWER
Answered 2019-Sep-17 at 12:20Your function map_pixel_to_vector is returning a list, not a tensor. You can make it into a tensor for example with tf.stack or tf.convert_to_tensor:
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Install fn.py
You can use fn.py like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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