Download JAR files tagged by before with all dependencies

The Finch HRIS API provides a unified way to connect to a multitide of HRIS systems. The API requires an access token issued by Finch. By default, Organization and Payroll requests use Finch's [Data Syncs](/developer-resources/Data-Syncs). If a request is made before the initial sync has completed, Finch will request data live from the provider. The latency on live requests may range from seconds to minutes depending on the provider and batch size. For automated integrations, Deductions requests (both read and write) are always made live to the provider. Latencies may range from seconds to minutes depending on the provider and batch size. Employer products are specified by the product parameter, a space-separated list of products that your application requests from an employer authenticating through Finch Connect. Valid product names are— - `company`: Read basic company data - `directory`: Read company directory and organization structure - `individual`: Read individual data, excluding income and employment data - `employment`: Read individual employment and income data - `payment`: Read payroll and contractor related payments by the company - `pay_statement`: Read detailed pay statements for each individual - `benefits`: Create and manage deductions and contributions and enrollment for an employer [](https://god.gw.postman.com/run-collection/21027137-08db0929-883d-4094-a9ce-dbf5a9bee4a4?action=collection%2Ffork&collection-url=entityId%3D21027137-08db0929-883d-4094-a9ce-dbf5a9bee4a4%26entityType%3Dcollection%26workspaceId%3D1edf19bc-e0a8-41e9-ac55-481a4b50790b)

The Finch HRIS API provides a unified way to connect to a multitide of HRIS systems. The API requires an access token issued by Finch. By default, Organization and Payroll requests use Finch's [Data Syncs](/developer-resources/Data-Syncs). If a request is made before the initial sync has completed, Finch will request data live from the provider. The latency on live requests may range from seconds to minutes depending on the provider and batch size. For automated integrations, Deductions requests (both read and write) are always made live to the provider. Latencies may range from seconds to minutes depending on the provider and batch size. Employer products are specified by the product parameter, a space-separated list of products that your application requests from an employer authenticating through Finch Connect. Valid product names are— - `company`: Read basic company data - `directory`: Read company directory and organization structure - `individual`: Read individual data, excluding income and employment data - `employment`: Read individual employment and income data - `payment`: Read payroll and contractor related payments by the company - `pay_statement`: Read detailed pay statements for each individual - `benefits`: Create and manage deductions and contributions and enrollment for an employer [](https://god.gw.postman.com/run-collection/21027137-08db0929-883d-4094-a9ce-dbf5a9bee4a4?action=collection%2Ffork&collection-url=entityId%3D21027137-08db0929-883d-4094-a9ce-dbf5a9bee4a4%26entityType%3Dcollection%26workspaceId%3D1edf19bc-e0a8-41e9-ac55-481a4b50790b)

The Finch HRIS API provides a unified way to connect to a multitide of HRIS systems. The API requires an access token issued by Finch. By default, Organization and Payroll requests use Finch's [Data Syncs](/developer-resources/Data-Syncs). If a request is made before the initial sync has completed, Finch will request data live from the provider. The latency on live requests may range from seconds to minutes depending on the provider and batch size. For automated integrations, Deductions requests (both read and write) are always made live to the provider. Latencies may range from seconds to minutes depending on the provider and batch size. Employer products are specified by the product parameter, a space-separated list of products that your application requests from an employer authenticating through Finch Connect. Valid product names are— - `company`: Read basic company data - `directory`: Read company directory and organization structure - `individual`: Read individual data, excluding income and employment data - `employment`: Read individual employment and income data - `payment`: Read payroll and contractor related payments by the company - `pay_statement`: Read detailed pay statements for each individual - `benefits`: Create and manage deductions and contributions and enrollment for an employer [](https://god.gw.postman.com/run-collection/21027137-08db0929-883d-4094-a9ce-dbf5a9bee4a4?action=collection%2Ffork&collection-url=entityId%3D21027137-08db0929-883d-4094-a9ce-dbf5a9bee4a4%26entityType%3Dcollection%26workspaceId%3D1edf19bc-e0a8-41e9-ac55-481a4b50790b)

This plugin provides support for textlint. <h3>What&rsquo;s the textlint?</h3> <p>See <a href="https://github.com/textlint/textlint">https://github.com/textlint/textlint</a></p> <h3>Usage</h3> <h4>Install textlint and rules</h4> <p>Of course, it assumes that nodejs and npm are installed.</p> <p>e.g.</p> <pre><code>$ mkdir txtlint $ cd txtlint $ npm init $ npm install textlint --save-dev $ npm install textlint-rule-max-ten textlint-rule-spellcheck-tech-word textlint-rule-no-mix-dearu-desumasu --save-dev </code></pre> <h4>Create .textlintrc</h4> <pre><code>$ touch .textlintrc </code></pre> <pre><code class="json">{ &quot;rules&quot;: { &quot;max-ten&quot;: { &quot;max&quot;: 3 }, &quot;spellcheck-tech-word&quot;: true, &quot;no-mix-dearu-desumasu&quot;: true } } </code></pre> <p>You can also set parameters to Options (see below).</p> <h4>Set textlint and .textlintrc paths</h4> <p>Set paths to the Options (see below).</p> <p>e.g.</p> <ul> <li>textlint Path: /path/to/txtlint/node_modules/.bin/textlint (textlint.cmd in Windows)</li> <li>.textlintrc Path: /path/to/textlint/.textlintrc</li> </ul> <h4>Open Action Items window</h4> <ul> <li>Click Window > Action Items.</li> <li>Click &ldquo;Show action items for currently edited file only&rdquo; icon.</li> <li>Open your markdown or text file.</li> </ul> <h3>Options</h3> <p>Tools > Options > Editor > textlint</p> <ul> <li>textlint Path: Absolute path to textlint</li> <li>.textlintrc Path: Absolute path to .textlintrc</li> <li>Options : You can set options for the textlint command</li> <li>Enable in HTML files: To use the html plugin, you can check this</li> <li>Refresh on Save: To scan the document on save, you can check this (Checked by default)</li> <li>Show Annotations: To show annotations in the glyph gutter, you can check this (Checked by default)</li> </ul> <h3>Actions</h3> <h4>Fix</h4> <p>You have to save your file before you run this action.<br/> If there is a fixable rule&rsquo;s error, you can fix it. Right-click an item > Click <code>Fix</code>.<br/> To refresh items, your document is saved once.</p> <h4>Fix All</h4> <p>You have to save your file before you run this action.<br/> If there are fixable rule&rsquo;s errors, you can fix them. Right-click an item > Click <code>Fix All</code>.<br/> This action runs <code>textlint --fix</code> command.</p> <h4>Refresh</h4> <p>You can refresh results forcibly by the following action: Right-click your editor > Click "textlint Refresh".</p> <p>You can also set the shortcut key(Tools > Options > Keymap). </p> <h3>NOTE</h3> <ul> <li>The plugin scans only current file.</li> <li>The plugin does not refresh results automatically. Please save your file or run the refresh action.</li> <li>Use <code>UTF-8</code> as file encoding and <code>LF</code> as line endings.</li> <li>This plugin may not work properly in Windows. (Please try to check above.)</li> <li>If you cannot get expected results, just try to run the <code>textlint</code> commands once in your CLI.</li> </ul>

This package currently contains classes for training multilayer perceptrons with one hidden layer, where the number of hidden units is user specified. MLPClassifier can be used for classification problems and MLPRegressor is the corresponding class for numeric prediction tasks. The former has as many output units as there are classes, the latter only one output unit. Both minimise a penalised squared error with a quadratic penalty on the (non-bias) weights, i.e., they implement "weight decay", where this penalised error is averaged over all training instances. The size of the penalty can be determined by the user by modifying the "ridge" parameter to control overfitting. The sum of squared weights is multiplied by this parameter before added to the squared error. Both classes use BFGS optimisation by default to find parameters that correspond to a local minimum of the error function. but optionally conjugated gradient descent is available, which can be faster for problems with many parameters. Logistic functions are used as the activation functions for all units apart from the output unit in MLPRegressor, which employs the identity function. Input attributes are standardised to zero mean and unit variance. MLPRegressor also rescales the target attribute (i.e., "class") using standardisation. All network parameters are initialised with small normally distributed random values.