Instantly share code, notes, and snippets.

# Jessica Stringhamjessstringham

• Sort options
Created May 16, 2018
View plot_wordvecs.py
 from bokeh.models import HoverTool from bokeh.plotting import figure, show, output_file, ColumnDataSource from bokeh.layouts import gridplot from bokeh.palettes import magma def plot(u, descs=index_to_interesting_subject): source = ColumnDataSource(data=dict( x1=u[:, 0], x2=u[:, 1], x3=u[:, 2],
Last active May 20, 2018
View ipynb_to_jekyll.py
 '''This is hacky code I use to convert Jupyter notebooks into Jekyll posts. Notebooks' first line should be # Title and Title will be used as the post's title. I convert LaTeX into the form MathJax needs. To make plots work, see get_maybe_image_code comment.
Last active Apr 8, 2018
View implicit_pasta.py
 image = mpimg.imread(IMAGE_LOCATION) # pasta the first image h, w, c = image.shape image = image.reshape(h // 2, 2, w, c) image = image.transpose(1, 0, 2, 3) image = np.hstack(image) # rotate the image image = image.transpose(1, 0, 2)
Last active Apr 8, 2018
View pasta_machine.py
 import numpy as np import matplotlib.image as mpimg import matplotlib.pyplot as plt PASTA_SIZE = 16 NUM_IMAGE_SPLITS = 2 IMAGE_LOCATION = 'something.png' image = mpimg.imread(IMAGE_LOCATION)
Last active Nov 24, 2017
View einsum.md

# np.einsum

I had this gross reshape/tensor product/transpose stuff on huge matrices, and I knew it was making intermediate copies of the matrices that I didn't want to. So I tried out np.einsum, and I think it actually turned out simpler than thinking through the other matrix manipulation.

Here are some quick notes.

## Real blogs/documentation

Created Nov 21, 2017
View hacking_the_matrix.py
 import numpy as np from numpy.lib.stride_tricks import as_strided # Makes a new matrix like this: # input: [0, 1, 2, 3] # output: [[0, 1], [1, 2], [2, 3]] # But, does it all in memory using as_strided, so it's speedy. # And if you make a mistake, you can see fake numbers
Last active Nov 17, 2017
Bayesian linear regression with polynomial basis functions
View 0.notes.md

Srry, this is a bit of an awkward format. I might move this and a few other demos to a repo.

This is code one could drop into Jupyter [1] notebook cells and draw some wicked-looking graphs. I wrote it to understand Bayesian linear regression (for this classMLPR, specifically this and this). No promises that I did it perfectly.

The graphs show samples from the posterior weights in pink.

You can’t perform that action at this time.