normalize

Solr Filter Query - String vs. Int

余生长醉 提交于 2019-12-07 06:14:11
问题 Say I'm trying to query a bunch of documents that have categories and I want to limit the queries to a specified category (as I understand it this would just be using the fq parameter (filter query). I was wondering if there is a performance improvement for having the parameter be an integer instead of a string or something as is usually the case with data? I would just err on the right side but I thought I'd double check in case it didn't matter very much and Solr performed some sort of

Is there an html css normalizer that works? [closed]

喜夏-厌秋 提交于 2019-12-06 14:45:34
问题 Closed. This question is off-topic. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed 4 years ago . Long ago, I wrote a style 'normalizer' program to scan the ASP/HTML code of a big pile of classic ASP pages (most of which were originally generated from MS-Word documents, so naturally they were littered with superflous stylesheets and massive one-off styles). The style normalizer generated a minimal set of

Issues replacing special characters in PHP string

青春壹個敷衍的年華 提交于 2019-12-06 08:37:28
I'm trying to replace the special characters in a PHP string with normal characters (as in replace ó with o and á with a). I tried using the PHP Normalizer::normalize function as in the following code: if (!Normalizer::isNormalized($word, Normalizer::FORM_C)) { echo "original: ".$word; $word = Normalizer::normalize($word, Normalizer::FORM_C); echo "\tnormalized: ".$word."<br />"; exit; // see if it worked without having to go through every file } However, Normalizer::normalize returned null and the output from that code was: original: adiós normalized: Since this method didn't seem to be

AVAudioRecorder normalize volume

会有一股神秘感。 提交于 2019-12-06 04:56:06
问题 I've got an app that records audio. I'm wondering how I can increase the gain. is there a way to normalize the audio or amplify it somehow? thanks, howie 回答1: Looks like I found a solution. According to the documentation the AVAudioPlayer volume can be between 0.0 and 1.0. Apparently it can be set to a value greater than 1.0. By increasing the volume I was able to achieve the same desired result. While it's not normalized, the gain is increased. 回答2: #import <AudioToolbox/AudioServices.h>

How to plot log normalized image using imshow () (matplotlib)? [closed]

試著忘記壹切 提交于 2019-12-05 22:44:26
Closed . This question needs details or clarity . It is not currently accepting answers. Want to improve this question? Add details and clarify the problem by editing this post . Closed 5 years ago . I understand the concept. But I think I am making a silly mistake. This is what I want (psuedo-ish code). This is for an exercise. I am unable to understand lower origin part and the syntax of the first two lines. norm = LogNorm(image.mean() + 0.5 * image.std(), image.max(), clip='True', cmap=cm.gray, origin="lower") image is a numpy array here. How to pass these norm and cmap parameters in

Plot two histograms on the same graph and have their columns sum to 100

≯℡__Kan透↙ 提交于 2019-12-05 21:55:51
I have two sets of different sizes that I'd like to plot on the same histogram. However, since one set has ~330,000 values and the other has about ~16,000 values, their frequency histograms are hard to compare. I'd like to plot a histogram comparing the two sets such that the y-axis is the % of occurrences in that bin. My code below gets close to this, except that rather than having the individual bin values sum to 1.0, the integral of the histogram sums to 1.0 (this is because of the normed=True parameter). How can I achieve my goal? I've already tried manually calculating the % frequency and

Solr Filter Query - String vs. Int

霸气de小男生 提交于 2019-12-05 10:58:15
Say I'm trying to query a bunch of documents that have categories and I want to limit the queries to a specified category (as I understand it this would just be using the fq parameter (filter query). I was wondering if there is a performance improvement for having the parameter be an integer instead of a string or something as is usually the case with data? I would just err on the right side but I thought I'd double check in case it didn't matter very much and Solr performed some sort of optimization under the hood? It would be much more convenient if I could just filter on string matches but.

Is it good to normalization/standardization data having large number of features with zeros

有些话、适合烂在心里 提交于 2019-12-05 07:33:19
问题 I'm having data with around 60 features and most will be zeros most of the time in my training data only 2-3 cols may have values( to be precise its perf log data). however, my test data will have some values in some other columns. I've done normalization/standardization(tried both separately) and feed it to PCA/SVD(tried both separately). I used these features in to fit my model but, it is giving very inaccurate results. Whereas, if I skip normalization/standardization step and directly feed

Is there an html css normalizer that works? [closed]

大兔子大兔子 提交于 2019-12-04 18:38:43
Closed. This question is off-topic . It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed 4 years ago . Long ago, I wrote a style 'normalizer' program to scan the ASP/HTML code of a big pile of classic ASP pages (most of which were originally generated from MS-Word documents, so naturally they were littered with superflous stylesheets and massive one-off styles). The style normalizer generated a minimal set of stylesheets and styles and a new 'sanitized' asp/html document, so that the sanitized document produced

Normalize/Standardize a numpy recarray

て烟熏妆下的殇ゞ 提交于 2019-12-04 11:49:03
问题 I wonder what the best way of normalizing/standardizing a numpy recarray is. To make it clear, I'm not talking about a mathematical matrix, but a record array that also has e.g. textual columns (such as labels). a = np.genfromtxt("iris.csv", delimiter=",", dtype=None) print a.shape > (150,) As you can see, I cannot e.g. process a[:,:-1] as the shape is one-dimensional. The best I found is to iterate over all columns: for nam in a.dtype.names[:-1]: col = a[nam] a[nam] = (col - col.min()) /