Second edition of Springer Book Python for Probability, Statistics, and Machine Learning

License: MIT License

Jupyter Notebook 99.96% Python 0.03% Dockerfile 0.01%

machine-learning springer-book-python probability-statistics python-modules

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python-for-probability-statistics-and-machine-learning-2e's Issues

Errors in 2.1.5 Independent Random Variables

Error trying to create environment using conda

When I try to create pyPSML environment in conda using environment.yaml I get a ResolvePackageNotFound:

conda env create -n pyPSML -f environment.yaml
Collecting package metadata (repodata.json): done
Solving environment: failed

ResolvePackageNotFound:
  - h5py==2.9.0=py37h7918eee_0
  - gxx_impl_linux-64==7.3.0=hdf63c60_1
  - c-ares==1.15.0=h7b6447c_1001
  - fastcache==1.1.0=py37h516909a_0
  - expat==2.2.5=he1b5a44_1003
  - tornado==6.0.3=py37h516909a_0
  - python==3.7.3=h33d41f4_1
  - readline==8.0=hf8c457e_0
  - grpcio==1.16.1=py37hf8bcb03_1
  - ecos==2.0.7=py37h3010b51_1000
  - tensorflow-base==1.14.0=mkl_py37h7ce6ba3_0
  - scikit-learn==0.21.2=py37hd81dba3_0
  - libsodium==1.0.16=h1bed415_0
  - scipy==1.2.1=py37h7c811a0_0
  - pygpu==0.7.6=py37h3010b51_1000
  - sqlite==3.29.0=hcee41ef_0
  - mpfr==4.0.2=ha14ba45_0
  - mistune==0.8.4=py37h7b6447c_0
  - jpeg==9c=h14c3975_1001
  - binutils_linux-64==2.31.1=h6176602_8
  - libxml2==2.9.9=h13577e0_2
  - mkl==2019.4=243
  - pthread-stubs==0.4=h14c3975_1001
  - freetype==2.10.0=he983fc9_1
  - dbus==1.13.6=he372182_0
  - cvxpy==1.0.24=py37he1b5a44_0
  - zlib==1.2.11=h516909a_1005
  - pyzmq==18.1.0=py37he6710b0_0
  - libiconv==1.15=h516909a_1005
  - libuuid==2.32.1=h14c3975_1000
  - multiprocess==0.70.8=py37h516909a_0
  - sip==4.19.8=py37hf484d3e_1000
  - pyrsistent==0.14.11=py37h7b6447c_0
  - gcc_impl_linux-64==7.3.0=habb00fd_1
  - statsmodels==0.10.0=py37hdd07704_0
  - gxx_linux-64==7.3.0=h553295d_8
  - libgfortran-ng==7.3.0=hdf63c60_0
  - osqp==0.5.0=py37hb3f55d8_0
  - libgcc-ng==9.1.0=hdf63c60_0
  - pandas==0.24.2=py37he6710b0_0
  - pcre==8.41=hf484d3e_1003
  - icu==58.2=hf484d3e_1000
  - gst-plugins-base==1.14.5=h0935bb2_0
  - kiwisolver==1.1.0=py37hc9558a2_0
  - theano==1.0.4=py37hf484d3e_1000
  - fontconfig==2.13.1=he4413a7_1000
  - scs==2.1.1.2=py37h4ff444d_0
  - gstreamer==1.14.5=h36ae1b5_0
  - gettext==0.19.8.1=hc5be6a0_1002
  - gmp==6.1.2=hf484d3e_1000
  - xorg-libxau==1.0.9=h14c3975_0
  - libffi==3.2.1=he1b5a44_1006
  - openssl==1.1.1c=h7b6447c_1
  - ncurses==6.1=hf484d3e_1002
  - bzip2==1.0.8=h516909a_0
  - intel-openmp==2019.4=243
  - libpng==1.6.37=hed695b0_0
  - numpy==1.17.0=py37h95a1406_0
  - qt==5.9.7=h52cfd70_2
  - xorg-libxdmcp==1.1.3=h516909a_0
  - glib==2.58.3=h6f030ca_1002
  - matplotlib==3.1.0=py37h5429711_0
  - tensorboard==1.14.0=py37hf484d3e_0
  - mkl-service==2.2.0=py37h516909a_0
  - gcc_linux-64==7.3.0=h553295d_8
  - tensorflow==1.14.0=mkl_py37h45c423b_0
  - xz==5.2.4=h14c3975_1001
  - gmpy2==2.1.0b1=py37h04dde30_0
  - pyqt==5.9.2=py37hcca6a23_2
  - zeromq==4.3.1=he6710b0_3
  - cvxpy-base==1.0.24=py37he1b5a44_0
  - libxcb==1.13=h14c3975_1002
  - libgpuarray==0.7.6=h14c3975_1003
  - yaml==0.1.7=had09818_2
  - pyyaml==5.1.2=py37h7b6447c_0
  - markupsafe==1.1.1=py37h14c3975_0
  - mpc==1.1.0=hb20f59a_1006
  - libprotobuf==3.8.0=hd408876_0
  - binutils_impl_linux-64==2.31.1=h6176602_1
  - wrapt==1.11.2=py37h7b6447c_0
  - hdf5==1.10.4=hb1b8bf9_0
  - protobuf==3.8.0=py37he6710b0_0
  - tk==8.6.9=hed695b0_1002
  - libstdcxx-ng==9.1.0=hdf63c60_0

Conditional_Expectation_Projection.ipynb

Señor Unpingco, it's really hard for me to understand the formula below the sentence: "The conditional expectation is the minimum mean squared error (MMSE) solution to the following problem...", if it's of the form $ \int_{R} (x-h(Y))^2 f_X(x) dx $ or $ \int_{R} (x-h(Y))^2 f_{X|Y}(x|y) dx $, it's more clear. It would be very kind of you if you could elaborate more on the formula.

computation in Page 49

Could you please chech the computation in Page 49? I think it should be

Typo Page 59

Hello - I think in the minimization problem you formulate in the middle of the page, you ought to be integrating against a density — meaning, you ought to include an “f(x)” after the (x-h(y))^2 term.

integral, p.48, line 5

I don't know how to interpret the integrand dP_X(dx). Please, give an explanation or a fix if this is a typo.

Third Edition of "Python for Probability, Statistics, and Machine Learning"

Sorry, I haven't found the github page for the 3rd edition of your above-mentioned book. So I'm making here a comment on a typo in the 3rd edition.
On page of 48 of the 3rd edition, "Lesbesgue theory" should read "Lebesgue theory".

ch. 2.1.1 "understanding probability density"

Hi José, I am sympathetic with the idea to base the chapter two on measure theory and the Lebesgue integral. But the example on p.40 and Fig 2.4 are in contradiction to the chapter title. Fig 2.1 doesn't show a density. Areas don't add up to 1. Furthermore the two measures have the same length 1. This does not motivate a learner to invest time in learning Lebesgue integration. Instead the graphic in Fig 2.1 I find a graphic in the german Wikipedia (https://de.wikipedia.org/wiki/Lebesgue-Integral) more instructive. The density is bimodal and the measures have different sizes. So the German graphic is even better that one of the English Wikipedia.
What is also missing is a hint or an example why the Lebesgue integral is necessary for understanding the chapters to come.
A further bonus would be Python code doing Lebsgue integration with an example where Riemann integration is not possible.
All the best, Claus

sympy stats.sample API has changed

The return type of sample has been changed to return an iterator
object since version 1.7. For more information see
sympy/sympy#19061

import numpy as np
from sympy import stats
# Eq constrains Z
samples_z7 = lambda : stats.sample(x, S.Eq(z,7)) 
#using 6 as an estimate
mn= np.mean([(6-samples_z7())**2 for i in range(100)]) 
#7/2 is the MSE estimate
mn0= np.mean([(7/2.-samples_z7())**2 for i in range(100)]) 
print('MSE=%3.2f using 6 vs MSE=%3.2f using 7/2 ' % (mn,mn0))

Error message

----> 2 mn = np.mean([(6 - samples_z7())**2 for i in range(100)])
TypeError: unsupported operand type(s) for -: 'int' and 'generator'```

Computation in Page 50 (Chapter 2.1.4)

Hi, I'm new in Github. I have a question about the computation in Page 50. I think it should be:

Could you please check this computation?

Page 15, Floating Point Numbers, Numpy, Chapter 1.

0.1 = 1.6 × 2^(−4)
In my humble opinion, should the both sides be equal?

unpingco / python-for-probability-statistics-and-machine-learning-2e Goto Github PK

python-for-probability-statistics-and-machine-learning-2e's People

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python-for-probability-statistics-and-machine-learning-2e's Issues

Errors in 2.1.5 Independent Random Variables

Error trying to create environment using conda

Conditional_Expectation_Projection.ipynb

computation in Page 49

Typo Page 59

integral, p.48, line 5

Third Edition of "Python for Probability, Statistics, and Machine Learning"

ch. 2.1.1 "understanding probability density"

sympy stats.sample API has changed

Computation in Page 50 (Chapter 2.1.4)

Page 15, Floating Point Numbers, Numpy, Chapter 1.

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