python-machine-learning | Python implementation of Andrew Ng & # 39 ; s machine | Artificial Intelligence library

 by   hujinsen HTML Version: Current License: MIT

kandi X-RAY | python-machine-learning Summary

kandi X-RAY | python-machine-learning Summary

python-machine-learning is a HTML library typically used in Artificial Intelligence applications. python-machine-learning has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

Python implementation of Andrew Ng's machine learning course exercises in coursera
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              python-machine-learning has a low active ecosystem.
              It has 71 star(s) with 22 fork(s). There are 3 watchers for this library.
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              The latest version of python-machine-learning is current.

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              python-machine-learning has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

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              python-machine-learning is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

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              python-machine-learning releases are not available. You will need to build from source code and install.
              Installation instructions are not available. Examples and code snippets are available.

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            Community Discussions

            QUESTION

            AttributeError: 'tensorflow.python.ops.rnn' has no attribute 'rnn'
            Asked 2019-Oct-22 at 07:01

            I am following this tutorial on Recurrent Neural Networks.

            This is the imports:

            ...

            ANSWER

            Answered 2019-Oct-22 at 07:01

            For people using the newer version of tensorflow, add this to the code:

            Source https://stackoverflow.com/questions/42311007

            QUESTION

            Iterating over rows of dataframe but keep each row as a dataframe
            Asked 2019-Sep-17 at 22:14

            I want to iterate over the rows of a dataframe, but keep each row as a dataframe that has the exact same format of the parent dataframe, except with only one row. I know about calling DataFrame() and passing in the index and columns, but for some reason this doesn't always give me the same format of the parent dataframe. Calling to_frame() on the series (i.e. the row) does cast it back to a dataframe, but often transposed or in some way different from the parent dataframe format. Isn't there some easy way to do this and guarantee it will always be the same format for each row?

            Here is what I came up with as my best solution so far:

            ...

            ANSWER

            Answered 2017-Apr-23 at 05:14

            Use groupby with a unique list. groupby does exactly what you are asking for as in, it iterates over each group and each group is a dataframe. So, if you manipulate it such that you groupby a value that is unique for each and every row, you'll get a single row dataframe when you iterate over the group

            Source https://stackoverflow.com/questions/43566975

            QUESTION

            Perceptron with python - TypeError appears but why?
            Asked 2019-Feb-09 at 00:20

            I am trying to code a Perceptron algorithm in python3. I am following a book example from Sebastian Raschka. His code can be found here:(https://github.com/rasbt/python-machine-learning-book-2nd-edition).

            Unfortunately I can not figure out why the error: TypeError: object() takes no parameters appears and how to handle it.

            I have used PyCharm first and now I am testing that issue with Jupiter step by step. I have even copied the fully code example from the GitHub repository offered from S. Raschka. But even than I get the same error, which is actually confusing me, because it means its probably not just a typo.

            ...

            ANSWER

            Answered 2019-Feb-07 at 21:39

            You defined the class Perzeptron but create an instance of Perceptron (c instead of z). It seems like you defined Perceptron earlier in your ipython session without defining the __init__ method taking two arguments.

            Source https://stackoverflow.com/questions/54582557

            QUESTION

            distribution plot in python
            Asked 2018-Jun-03 at 21:54

            I need your help in understanding the distribution plot. I was going through tutorial on this link. At the end of the post they have mentioned:

            We can see from the graph that most of the times the predictions were correct (difference = 0).

            So I am not able to understand how are they analyzing the graph.

            ...

            ANSWER

            Answered 2018-Jun-03 at 20:28

            You can think of the density graph that it shows the relative number of occurrences of the data at given values. The values in question are differences between observed and fitted variable values. If the fit was perfect, all the differences would have been 0, and there would have been just one bar at 0. The fit is not perfect, and there are some differences greater or smaller than 0, but they are not too far from zero.

            The conclusion authors draw is probably too strong: the graph does not prove the differences are close to zero, but it suggests the differences are centered around zero. Generally, it is a good result for linear regression.

            Source https://stackoverflow.com/questions/50669200

            QUESTION

            Can I safely assign to `coef_` and other estimated parameters in scikit-learn?
            Asked 2017-Sep-20 at 12:10

            scikit-learn suggests the use of pickle for model persistence. However, they note the limitations of pickle when it comes to different version of scikit-learn or python. (See also this stackoverflow question)

            In many machine learning approaches, only few parameters are learned from large data sets. These estimated parameters are stored in attributes with trailing underscore, e.g. coef_

            Now my question is the following: Can model persistence be achieved by persisting the estimated attributes and assigning to them later? Is this approach safe for all estimators in scikit-learn, or are there potential side-effects (e.g. private variables that have to be set) in the case of some estimators?

            It seems to work for logistic regression, as seen in the following example:

            ...

            ANSWER

            Answered 2017-Sep-20 at 12:10

            Setting the estimated attributes alone is not enough - at least in the general case for all estimators.

            I know of at least one example where this would fail. LinearDiscriminantAnalysis.transform() makes use of the private attribute _max_components:

            Source https://stackoverflow.com/questions/46316031

            QUESTION

            tensorflow: tf.split is given weird parameters
            Asked 2017-Aug-19 at 19:42

            Here is code(from here):

            ...

            ANSWER

            Answered 2017-Aug-19 at 19:42

            By documenation this should be axis...but that can't be, right?

            From tensorflow 1.0 onwards, the first argument of tf.split is not the axis, but I assume that the code was written using an older version where the first argument is indeed the axis.

            Isn't x one dimensional?

            x is not one dimensional. Right before the call to tf.split, x is reshaped from 3 to 2 dimensions with this statement:

            Source https://stackoverflow.com/questions/45774938

            QUESTION

            Path to existing file in root folder not found on Windows
            Asked 2017-Jul-29 at 21:37

            I extracted these 4 files in D:

            ...

            ANSWER

            Answered 2017-Jul-29 at 19:40

            QUESTION

            Understanding LSTM model using tensorflow for sentiment analysis
            Asked 2017-Jun-16 at 17:20

            I am trying to learn LSTM model for sentiment analysis using Tensorflow, I have gone through the LSTM model.

            Following code (create_sentiment_featuresets.py) generates the lexicon from 5000 positive sentences and 5000 negative sentences.

            ...

            ANSWER

            Answered 2017-Jun-16 at 17:20

            This is loaded question. Let me try to put it in simple English hiding all the complicated inner details:

            A simple Unrolled LSTM model with 3 steps is shown below. Each LSTM cell takes an input vector and the hidden output vector of the previous LSTM cell and produces an output vector and the hidden output for the next LSTM cell.

            A concise representation of the same model is shown below.

            LSTM models are sequence to sequence models, i.e, they are used for problems when a sequence has to be labeled with an another sequence, like POS tagging or NER tagging of each word in a sentence.

            You seem to be using it for classification problem. There are two possible ways to use LSTM model for classification

            1) Take the output of all the states (O1, O2 and O3 in our example) and apply a softmax layer with softmax layer output size being equal to number of classes (2 in your case)

            2) Take the output of the last state (O3) and apply a softmax layer to it. (This is what you are doing in your cod. outputs[-1] return the last row in the outputs)

            So we back propagate (Backpropagation Through Time - BTT) on the error of the softmax output.

            Coming to the implementation using Tensorflow, lets see what is the input and output to the LSTM model.

            Each LSTM takes an input, but we have 3 such LSTM cells, So the input (X placeholder) should be of size (inputsize * time steps). But we don't calculate error for single input and BTT for it, but instead we do it on a batch of input - output combinations. So the Input of LSTM will be (batchsize * inputsize * time steps).

            A LSTM cells is defined with the size of hidden state. The size of output and the hidden output vector of the LSTM cell will be same as the size of the hidden states (Check LSTM internal calcuations for why!). We then define an LSTM Model using a list of these LSTM cells where the size of the list will be equal to the number of unrolling of the model. So we define the number of unrolling to be done and the size of input during each unrolling.

            I have skipped lots of things like how to handle variable length sequence, sequence to sequence error calcuations, How LSTM calcuates output and hidden output etc.

            Coming to your implementation, you are applying a relu layer before the input of each LSTM cell. I dont understand why you are doing that but I guess you are doing it to map your input size to that of the LSTM input size.

            Coming to your questions:

            1. x is the placeholder (tensor/matrix/ndarray) of size [None, input_vec_size, 1]. i.e it can take variable number of rows but each row with input_vec_size columns and each element being a vector is size 1. Normally placeholders are defined with "None" in the rows so that we can vary the batch size of the input.

            lets say input_vec_size = 3

            You are passing a ndarray of size [128 * 3 * 1]

            x = tf.transpose(x, [1,0,2]) --> [3*128*1]

            x = tf.reshape(x, [-1, 1]) --> [384*1]

            h_layer['weights'] --> [1, 128]

            x= tf.nn.relu(tf.matmul(x, h_layer['weights']) + h_layer['biases']) --> [384 * 128]

            1. No input size are hidden size are different. LSTM does a set of operations on the input and previous hidden output and given an output and next hidden output both of which are of size hidden size.

            2. x = tf.placeholder('float', [None, input_vec_size, 1])

            It defines a tensor or ndarray or variable number of rows, each rows has input_vec_size columns an and each value is a single value vector.

            x = tf.reshape(x, [-1, 1]) --> reshapes the input x into a matrix of size fixed to 1 column and any number of rows.

            1. batch_x = batch_x.reshape(batch_size ,input_vec_size, 1)

            batch_x.reshape will fail if number of values in batch_x != batch_size*input_vec_size*1. This might be the case for last batch because len(train_x) might not be a multiple of batch_size resulting in the non fully filled last batch.

            You can avoid this problem by using

            Source https://stackoverflow.com/questions/44386348

            QUESTION

            Why isn't `model.fit` defined in scikit-learn?
            Asked 2017-Jan-06 at 19:51

            I am following step 3 of this example:

            ...

            ANSWER

            Answered 2017-Jan-06 at 19:25

            fit(x,y) is a method that can be used on an estimator.

            In order to be able to use this method on model you would have to create model first and make sure its of an estimator class.

            Documentation

            Source https://stackoverflow.com/questions/41512715

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