uncleflo
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python - How do I fit a sine curve to my data with pylab and numpy? - Stack Overflow
Saved by uncleflo on May 15th, 2021.
I am trying to show that economies follow a relatively sinusoidal growth pattern. I am building a python simulation to show that even when we let some degree of randomness take hold, we can still produce something relatively sinusoidal. I am happy with the data I'm producing, but now I'd like to find some way to get a sine graph that pretty closely matches the data. I know you can do polynomial fit, but can you do sine fit? You can use the least-square optimization function in scipy to fit any arbitrary function to another. In case of fitting a sin function, the 3 parameters to fit are the offset ('a'), amplitude ('b') and the phase ('c').
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VHF radio channels [Archive] - Yachting and Boating World Forums
Saved by uncleflo on October 12th, 2019.
I have just bought a yacht back from France with a NAVICOM RT 450 DSC a couple of years old. But I cannot get the full range of radio channels, especially M1 and M2, is it me or the radio? Is the radio set on USA rather than International channels? There's usually a means of switching from one to the other.
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Marine VHF radio - Wikipedia
Saved by uncleflo on October 12th, 2019.
Marine VHF radio refers to the radio frequency range between 156 and 174 MHz, inclusive. The "VHF" signifies the very high frequency of the range. In the official language of the International Telecommunication Union the band is called the VHF maritime mobile band. In some countries additional channels are used, such as[1] the L and F channels for leisure and fishing vessels in the Nordic countries (at 155.5–155.825 MHz).
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How TF-IDF algorithm determines keyword importance - arbitrue Blog
Saved by uncleflo on December 23rd, 2018.
There are many tools in the developer’s toolbox when it comes to automatic data extraction. A good example is TF-IDF algorithm (Term Frequency – Inverse Document Frequency) which helps the system understand the importance of keywords extracted using OCR. Here’s how TF-IDF can be used for invoice and receipt recognition. In this article we focus on other techniques in order to make this text file “understandable” to a computer. For this purpose, we must delve into the world of NLP or Natural Language Processing. We will focus mainly on how we can transform our file of raw text into a format that will easily be understandable by our algorithm. In a nutshell, TF-IDF is a technique for understanding how important a word is in a document which is often used as a weighting factor for numerous use cases. TF-IDF takes under consideration how frequent a word appears in a single document in relation to how frequent that word is in general. Search engines can use TF-IDF to determine which results are the most relevant for a search query.
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2 Sentiment analysis with tidy data | Text Mining with R
Saved by uncleflo on December 23rd, 2018.
In the previous chapter, we explored in depth what we mean by the tidy text format and showed how this format can be used to approach questions about word frequency. This allowed us to analyze which words are used most frequently in documents and to compare documents, but now let’s investigate a different topic. Let’s address the topic of opinion mining or sentiment analysis. When human readers approach a text, we use our understanding of the emotional intent of words to infer whether a section of text is positive or negative, or perhaps characterized by some other more nuanced emotion like surprise or disgust. We can use the tools of text mining to approach the emotional content of text programmatically, as shown in Figure 2.1.
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Term Frequency and Inverse Document Frequency (tf-idf) Using Tidy Data Principles
Saved by uncleflo on December 23rd, 2018.
A central question in text mining and natural language processing is how to quantify what a document is about. Can we do this by looking at the words that make up the document? One measure of how important a word may be is its term frequency (tf), how frequently a word occurs in a document. There are words in a document, however, that occur many times but may not be important; in English, these are probably words like “the”, “is”, “of”, and so forth. We might take the approach of adding words like these to a list of stop words and removing them before analysis, but it is possible that some of these words might be more important in some documents than others. A list of stop words is not a sophisticated approach to adjusting term frequency for commonly used words. Another approach is to look at a term’s inverse document frequency (idf), which decreases the weight for commonly used words and increases the weight for words that are not used very much in a collection of documents. This can be combined with term frequency to calculate a term’s tf-idf, the frequency of a term adjusted for how rarely it is used. It is intended to measure how important a word is to a document in a collection (or corpus) of documents. It is a rule-of-thumb or heuristic quantity; while it has proved useful in text mining, search engines, etc., its theoretical foundations are considered less than firm by information theory experts.
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tf–idf - Wikipedia
Saved by uncleflo on December 23rd, 2018.
In information retrieval, tf–idf or TFIDF, short for term frequency–inverse document frequency, is a numerical statistic that is intended to reflect how important a word is to a document in a collection or corpus. It is often used as a weighting factor in searches of information retrieval, text mining, and user modeling. The tf–idf value increases proportionally to the number of times a word appears in the document and is offset by the number of documents in the corpus that contain the word, which helps to adjust for the fact that some words appear more frequently in general. Tf–idf is one of the most popular term-weighting schemes today; 83% of text-based recommender systems in digital libraries use tf–idf. Variations of the tf–idf weighting scheme are often used by search engines as a central tool in scoring and ranking a document's relevance given a user query. tf–idf can be successfully used for stop-words filtering in various subject fields, including text summarization and classification. One of the simplest ranking functions is computed by summing the tf–idf for each query term; many more sophisticated ranking functions are variants of this simple model.
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Weighting words using Tf-Idf - NLP-FOR-HACKERS
Saved by uncleflo on December 23rd, 2018.
If I ask you “Do you remember the article about electrons in NY Times?” there’s a better chance you will remember it than if I asked you “Do you remember the article about electrons in the Physics books?”. Here’s why: an article about electrons in NY Times is far less common than in a collection of physics books. It is less likely to stumble upon the “electron” concept in NY Times than in a physics book. Let’s consider now the scenario of a single article. Suppose you read an article and you’re asked to rank the concepts found in the article by importance. The chances are you’ll basically order the concepts by frequency. The reason is simply that important stuff would be mentioned repeatedly because the narrative gravitates around them. Combining the 2 insights, given a term, a document and a collection of documents we can loosely say that:importance ~ appearances(term, document) / count(documents containing term in collection).
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