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A CSV file). It's much quicker to train on precomputed unigram frequencies. However, word bigram frequencies can't be determined from word unigram frequencies, so the highest possible degree in your word n-gram range is 1 if fitting on the unigram frequencies. Therefore, if the user does not choose word n-grams higher than degree 1, then the solver is fitted on the pre-computed unigrams (see FREQS_PATH in defaults.py); otherwise, it's fitted on the corpus directly (CORPUS_PATH).Using the default dataIf you'd like to use the default data to fit the solver, then download the data here and put the zip files into the following directory structure and then run make_data/make_kaggle_news_data.py. This is a manual process because you need to create a Kaggle account to access the data.data|-- raw |-- corpora |-- kaggle_news |-- articles1.csv.zip |-- articles2.csv.zip |-- articles3.csv.zipUsing custom dataTo use your own custom data to fit the solver, you can do one of the following:Change the default paths. If you have a corpus but not unigram frequencies, then modify CORPUS_PATH (where you saved the corpus data) and FREQS_PATH (where you want the unigram frequency data to be generated) in defaults.py. Then run corpus_to_freqs.py. If you have both corpus data and unigram frequency data, then just modify the paths just mentioned.Specify paths each time. That is, specify either --freqs_path or --docs_path each time you fit the solver.Dependenciestqdm for progress barsTODOSStore data somewhere and write script to download it. (And possibly change the data source.)Use random search to find better set of default

Skip to content Navigation Menu GitHub Copilot Write better code with AI Security Find and fix vulnerabilities Actions Automate any workflow Codespaces Instant dev environments Issues Plan and track work Code Review Manage code changes Discussions Collaborate outside of code Code Search Find more, search less Explore Learning Pathways Events & Webinars Ebooks & Whitepapers Customer Stories Partners Executive Insights GitHub Sponsors Fund open source developers The ReadME Project GitHub community articles Enterprise platform AI-powered developer platform Pricing Provide feedback Saved searches Use saved searches to filter your results more quickly //voltron/issues_fragments/issue_layout;ref_cta:Sign up;ref_loc:header logged out"}"> Sign up Notifications You must be signed in to change notification settings Fork 487 Star 4.2k DescriptionIt would be better to able user to select folder where to save the files, because using temp folder is not nice option in most of cases (because Unigram stores there lots of its staff).I have not investigated much, but it seems be easy to provide such a feature.if you would have very little time on such a feature I will be glad to contribute to Unigram, but after New Year 🎅

For several functions (stop music, wifi, bluetooth, etc.)Spectrum Analyzer = a decibel meterTouch Lock = a button to switch off the screen (you can do the same by double tapping the software Navigation Bar, at the bottom of the Home screen).Tripadvisor5) GLANCE SCREEN (Always-on display): it is still working, and you can display up to 5 apps' quick status, according to the personalization settings under the “Lock Screen” menu. As my favourite 5, I choose to show the status of Phone, Battery Bar Graph app, Unigram app, Messages, and Clock & Alarms. Plus, you can choose an app to show detailed status. My choice is the Microsoft Weather app, because it can display in the lock screen (and same in Glance screen) up to three lines of weather content.6) MAPS: the in-built app still works fine, although it has no live tile.7) FUTURE-PROOF. You can install the W10M Group app, designed by the Windows 10 Mobile (Lumia) Telegram group. See also Steve’s article on the group The app, also called W.U.T. (Windows Universal Tool), is a kind of repository and allows you to install many different apps. You can see a presentation of the project in this Youtube video.In summary...As you can see, all built-in Microsoft apps are still working, with updated live tiles, and you can install more apps via the Microsoft Store, or - if you are geeky enough - sideload apps using the in-built WP10 function (go to Menu, Update and Security, For Developers, and then select

The optimization, while the word n-grams help the algorithm "lock in" on good mappings at the end. uniform(0, 1): # softmax best_mapping = mapping best_score = score decrypted = best_mapping.translate(encrypted) return best_mapping, decrypted">def simulated_annealing(encrypted, num_iters): """Python-style pseudo(-ish)code for simulated annealing algorithm.""" best_mapping = Mapping() best_score = compute_score(encrypted) for i in range(num_iters): temp = temp_list[i] # from scheduler num_swaps = swap_list[i] # from scheduler mapping = best_mapping.random_swap(num_swaps) text = mapping.translate(encrypted) score = compute_score(text) score_change = score - best_score if exp(-score_change / temp) > uniform(0, 1): # softmax best_mapping = mapping best_score = score decrypted = best_mapping.translate(encrypted) return best_mapping, decryptedTo decrease the number of swaps over time, I use a Poisson distribution with a lambda parameter that decreases linearly. The scheduler for the number of swaps isdef schedule_swaps(lamb_start, lamb_end, n): for l in linspace(lamb_start, lamb_end, n): yield rpoisson(l) + 1 where lamb_start is the starting lambda (at the beginning of the optimization), lamb_end is the ending lambda, n is the number of iterations in the optimization, linspace is a function that returns evenly spaced numbers over a specified interval (a basic version of numpy's implementation), and rpoisson is a function that draws a random sample from a Poisson distribution given a lambda parameter. Note that a lambda greater than zero gives you additional swaps; if lamb_start and lamb_end are both zero, then in every iteration you swap only once.DataThe solver can be fitted using either a corpus of documents (as a text file) or a list of precomputed word unigram frequencies (as

May not deliver on the promise of deep insights.This is why at AddMaple, we have centered our approach on the principle of intuitive interaction. We’ve designed our word clouds to look good, be intuitive to work with and be quick to dive into the details and explore connections between words.We also don’t hide the actual raw free text responses from users - once a dataset has been filtered down using our interactive word clouds it is easy to switch to a table view focussed on the column being analyzed.Stop WordsIn our word clouds, we exclude "stop words," which are common but less informative words like 'the', 'is', and 'on'. This helps highlight more meaningful words, making the data analysis clearer and more relevant. Currently we only support English stop words, but let us know and we can add support for your language.Our recommended workflowLoad the Data: Begin by loading your dataset into AddMaple. You can load from CSV or SAV files or bring your data in directly from Typeform, Survey Monkey or Google Drive. AddMaple will automatically detect your column types and prepare text columns for unigram based analysis.Instant Word Cloud Visualization: For each text column, a word cloud is auto-generated. Crafted from unigrams, this cloud disregards stop words and captures the essence of the responses by highlighting the most frequently used terms. Whether respondents are discussing generic concepts or mentioning specific product names, you'll be able to see the key words used at a glance.Interactive Exploration: Dive deeper into the data by clicking around. Employ the filtering options to narrow down topics or themes. The beauty of AddMaple's interface is the fluidity it offers; see a word you are interested in, click it and instantly see related words. Deep Dive with Table View: After honing in on a subset of data through filters, explore the raw data in our table view. By simply clicking on the 'table' link, you're presented with a neatly organized table showcasing the raw responses related to your filters. Each filtered word is highlighted to make it easy to see how they were actually used in the raw data. This focused approach, devoid of other distracting columns, makes it easy to explore lots of raw text responses in an efficient manner.From Macro to MicroAddMaple's intuitive design takes you on a journey from overarching themes to the nuanced sentiments of individual respondents. This gradient of insights ensures that while you grasp the bigger picture, you never lose touch with the unique perspectives that often hold the key to understanding your audience better.If you think we should approach this problem another way, we’d love to hear from you. Designing a data analysis tool that is powerful, but intuitive is a big challenge. Hopefully this post explains the why behind our word cloud interface. The next time you have a dataset with free text responses try exploring it with AddMaple.

A Deep Dive into Ngrams, Filters, and Contextual AnalysisBefore you roll your eyes at the mention of word clouds, please let us explain why ours are useful with the addition of a few extra tricks. We can all agree that analyzing unstructured text is difficult. And we can agree that free text data could contain hidden insights. For example that open ended survey question might have brought something to your attention you didn't know to ask about in the multiple choice questions. The review data waiting to be explored might help you understand how your customers view your product compared to your competitors. And getting a handle on support data could help you prioritize the next feature because you have a greater understanding of that customer need in the first placeExploring unstructured free text (e.g. free text responses to a survey question) is difficult. At AddMaple we’ve explored a few different approaches and would like to share why we think interactive word clouds are an extremely powerful tool.There is a lot of evidence that “conversational surveys” or allowing users to enter free text answers is valuable. Rather than restricting users to choosing a preselected list of answers (which by definition will be limited to what we know at the time), free-text answers allow you to discover what users really think about a topic. As well as surveys, analyzing customer support messages or customer reviews is extremely valuable. Oftentimes a decision is made to leave out open questions because of the analytical challenges they bring but dear reader, we have made this much simpler to tackle. At AddMaple we are big believers in thematic analysis (and the role that AI can play in it), however, in this post, we will explain a simpler and quicker way to explore a large free text dataset. We’ll explore n-grams, stemming, and interactive word clouds and we’ll explain why we made the choices that we did.N-grams, Unigrams, Bigrams, and Trigrams ExplainedN-grams are commonly used in natural language processing, here is a brief explanation of what they are:N-grams: A contiguous sequence of 'n' items from a given text sample. In text, an item often means a word. The 'n' in n-gram denotes how many words are grouped together, aiding in understanding language structure.Unigrams: Single words. In the phrase "I love ice cream", unigrams are "I", "love", "ice", and "cream".Bigrams: Pairs of words. From the same phrase, the bigrams are: "I love", "love ice", and "ice cream".Trigrams: Sequences of three words. Using the aforementioned example, trigrams are: "I love ice" and "love ice cream".By using n-grams, we can capture linguistic patterns, with unigrams highlighting individual words, while bigrams and trigrams offer contextual insight by examining words in pairs or groups of three.What size n-gram to use?Traditionally, many analysts have championed the use of bigrams and trigrams when trying to understand the thematic structure of a text. These combinations of two or three words can provide context that a single word - or unigram - might lack. For