normalize

问题 I'm using matplotlib to plot log-normalized images but I would like the original raw image data to be represented in the colorbar rather than the [0-1] interval. I get the feeling there's a more matplotlib'y way of doing this by using some sort of normalization object and not transforming the data beforehand... in any case, there could be negative values in the raw image. import matplotlib.pyplot as plt import numpy as np def log_transform(im): '''returns log(image) scaled to the interval [0

问题 This is a sample json file I'm working with with 2 records: [{"Time":"2016-01-10", "ID" :13567, "Content":{ "Event":"UPDATE", "Id":{"EventID":"ABCDEFG"}, "Story":[{ "@ContentCat":"News", "Body":"Related Meeting Memo: Engagement with target firm for potential M&A. Please be on call this weekend for news updates.", "BodyTextType":"PLAIN_TEXT", "DerivedId":{"Entity":[{"Id":"Amy","Score":70}, {"Id":"Jon","Score":70}]}, "DerivedTopics":{"Topics":[ {"Id":"Meeting","Score":70}, {"Id":"Performance",

问题 I just discovered the json_normalize function which works great in taking a JSON object and giving me a pandas Dataframe. Now I want the reverse operation which takes that same Dataframe and gives me a json (or json-like dictionary which I can easily turn to json) with the same structure as the original json. Here's an example: https://hackersandslackers.com/json-into-pandas-dataframes/. They take a JSON object (or JSON-like python dictionary) and turn it into a dataframe, but I now want to

问题 I am looking for the way to normalise the list of titles. The title is normalized to be stored in a database as a sort and look up key. "Normalize" means many things such as converting to lowercase, removing the roman accent character, or removing preceding "the", "a" or "an". In iOS or Mac, NSString class has stringByFoldingWithOptions:locale: method to get the folding version of string. NSString Class Reference - stringByFoldingWithOptions:locale: In Java, java.uril.Collator class seems to

问题 I have been trying to normalize a very nested json file I will later analyze. What I am struggling with is how to go more than one level deep to normalize. I went through the pandas.io.json.json_normalize documentation, since it does exactly what I want it to do. I have been able to normalize part of it and now understand how dictionaries work, but I am still not there. With below code I am able to get only the first level. import json import pandas as pd from pandas.io.json import json

问题 I'd like to know do I normalize a URL in python. For example, If I have a url string like : "http://www.example.com/foo goo/bar.html" I need a library in python that will transform the extra space (or any other non normalized character) to a proper URL. 回答1: 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

问题 I'd like to know do I normalize a URL in python. For example, If I have a url string like : "http://www.example.com/foo goo/bar.html" I need a library in python that will transform the extra space (or any other non normalized character) to a proper URL. 回答1: 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

问题 I have a ggplot stat_bin2d "heatmap". library(ggplot2) value<-rep(1:5, 1000) df<-as.data.frame(value) df$group<-rep(1:7, len=5000) df<-df[sample(nrow(df), 3000), ] ggplot(df, aes(factor(group), factor(value))) +stat_bin2d() I have tried to add fill to aes: aes(factor(group), factor(value),fill = (..count..)/mean(..count..)) as a way to mimic ..density.. (not accepted) does not seem to be accepted, but it is not what I am wanting - it seems to divide by the sum of the counts for the whole df.

问题 I'm trying to train a convolutional neural network similar to Facenet and Openface. My model is inspired by VGG-16 (posted below). The problem is that, when I use output = tf.nn.l2_normalize(output, 0) before returning the output, the accuracy drops significantly. I get near 98 percent accuracy without l2_normalize , however, the openface model described here uses it. These are my results with output = tf.nn.l2_normalize(output, 0) : epoch 0 loss 0.36241 acc 45.59 nr_test_examples 6400 total

问题 Im fairly new to SQL, so difficult for me to know whats good, bad, better or best design. I have a SQL 2008 database which I'm using along with Entity Framework 4.3. I'm trying to normalize my database. In my design I have 2 tables Applications and AcceptedApplications. AcceptedApplications is simply an extension of the Applications table, for, you guessed it, AcceptedApplications. This simply contains further information that is not pertinent to rejected applications. There is a foreign key