Yes, there is! Use LogNorm. Here is a code excerpt from a utility that I wrote to display confusion matrices on a log scale. from pylab import figure, cm from matplotlib.colors import LogNorm # C = some matrix f = figure(figsize=(6.2, 5.6)) ax = f.add_axes([0.17, 0.02, 0.72, 0.79]) axcolor = f.add_axes([0.90, 0.02, 0.03, 0.79]) im … Read more
Yes, there is! Use LogNorm. Here is a code excerpt from a utility that I wrote to display confusion matrices on a log scale. from pylab import figure, cm from matplotlib.colors import LogNorm # C = some matrix f = figure(figsize=(6.2, 5.6)) ax = f.add_axes([0.17, 0.02, 0.72, 0.79]) axcolor = f.add_axes([0.90, 0.02, 0.03, 0.79]) im … Read more
If I understand correctly, what you want to do is divide by the maximum value in each column. You can do this easily using broadcasting. Starting with your example array: import numpy as np x = np.array([[1000, 10, 0.5], [ 765, 5, 0.35], [ 800, 7, 0.09]]) x_normed = x / x.max(axis=0) print(x_normed) # [[ … Read more
Using the mean and std of Imagenet is a common practice. They are calculated based on millions of images. If you want to train from scratch on your own dataset, you can calculate the new mean and std. Otherwise, using the Imagenet pretrianed model with its own mean and std is recommended.
Have a look at this module: werkzeug.utils. (now in werkzeug.urls) The function you are looking for is called “url_fix” and works like this: >>> from werkzeug.urls import url_fix >>> url_fix(u’http://de.wikipedia.org/wiki/Elf (Begriffsklärung)’) ‘http://de.wikipedia.org/wiki/Elf%20%28Begriffskl%C3%A4rung%29′ It’s implemented in Werkzeug as follows: import urllib import urlparse def url_fix(s, charset=”utf-8″): “””Sometimes you get an URL by a user that just … Read more
No, you shouldn’t. A modified version of normalize.css is already included in Bootstrap 2.3.2. Here it is. UPDATE: the docs makes this even more explicit, and now they use the original code, not their fork. UPDATE: (Bootstrap 4) For improved cross-browser rendering, we use Reboot to correct inconsistencies across browsers and devices while providing slightly … Read more
one easy way by using Pandas: (here I want to use mean normalization) normalized_df=(df-df.mean())/df.std() to use min-max normalization: normalized_df=(df-df.min())/(df.max()-df.min()) Edit: To address some concerns, need to say that Pandas automatically applies colomn-wise function in the code above.