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By activating your account, you will create a login and password. You only need to activate your account once. See AllSee AllSee AllSee AllSee AllSee AllSee AllStrength or resistance training challenges your muscles with a stronger-than-usual counterforce, such as pushing against a wall or lifting a dumbbell or pulling on a resistance band.
Using progressively heavier weights or increasing resistance makes muscles stronger. This kind of exercise increases muscle mass, tones muscles, and strengthens bones. It also helps you maintain the strength you need for everyday activities lifting groceries, climbing stairs, rising from a chair, or rushing for the bus. The current national guidelines for physical activity recommend strengthening exercises for all major muscle groups (legs, hips, back, chest, abdomen, shoulders, and arms) at least twice a week.
One set usually 8 to 12 repetitions of the same movement per session is effective, though some evidence suggests that two to three sets may be better. Your muscles need at least 48 hours to recover between strength training sessions. For detailed workouts and more on the benefits of exercise and how to develop a plan stick with it, buy Exercise: A program you can live with, a Special Health Report from Harvard Medical School.
Building muscle isn't just for individuals into fitness as a hobby. Muscle strength is crucial for good health. Strong muscles strengthen bones, control blood sugar, improve cholesterol levels, maintain a healthy weight, reduce joint pain, and fight mild depression. Shopping Cart Description Qty Price The Harvard Medical School 6-Week Plan for Healthy Eating (Print - Free U. Tips to measure your blood pressure correctly To determine whether you have hypertension, a medical professional will take a blood pressure reading.Example: "name": "Price" names optional Array Names for each of the new fields generated.
Basically, a Flatline expresion can easily be translated to its json-like variant and vice versa by just changing parentheses to brackets, symbols to quoted strings, and adding commas to separate each sub-expression. For example, the following two expressions are the same for BigML. If you specify both sampling and filtering arguments, the former are applied first.
As with filters applied to datasources, dataset filters can use the full Flatline language to specify the boolean expression to use when sifting the input.
Flatline performs type inference, and will in general figure out the proper optype for the generated fields, which are subsequently summarized by the dataset creation process, reaching then their final datatype (just as with a regular dataset created from a datasource).
In case you need to fine-tune Flatline's inferences, you can provide an optype (or optypes) key and value in the corresponding output field entry (together with generator and names), but in general this shouldn't be needed. Samples Last Updated: Monday, 2017-10-30 10:31 A sample provides fast-access to the raw data of a dataset on an on-demand basis.
When a new sample is requested, a copy of the dataset is stored in a special format in an in-memory cache. Multiple and different samples of the data can then be extracted using HTTPS parameterized requests by sampling sizes and simple query string filters. That is to say, a sample will be available as long as GETs are requested within periods smaller than a pre-established TTL (Time to Live).
The expiration timer of a sample is reset every time a new GET is received. If requested, a sample can also perform linear regression and compute Pearson's and Spearman's correlations for either one numeric field against all other numeric fields or between two specific numeric fields.
You can also list all of your samples. You can also use curl to customize a new sample with a name. Once a sample has been successfully created it will have the following properties.
Through the status field in the sample you can determine when the sample has been fully processed and ready to be used. Thus when retrieving a sample, it's possible to specify that only a subset of fields be retrieved, by using any combination of the following parameters in the query string (unrecognized parameters are ignored): Fields Filter Parameters Parameter TypeDescription fields optional Comma-separated list A comma-separated list of field IDs to retrieve.
A sample might be composed of thousands or even millions of rows. Thus when retrieving a sample, it's possible to specify that only a subset of rows be retrieved, by using any combination of the following parameters in the query string (unrecognized parameters are ignored). BigML will never return more than 1000 rows in the same response. However, you can send additional request to get different random samples. Filtering Rows from a Sample Parameters Parameter TypeDescription.
As with inclusion, it's possible to include or exclude the boundaries of the specified interval using square or round brackets Example: ". One of the limits can be omitted. This can be useful, for instance, when you're performing various GET requests and want to compute the union of the returned regions.
You'll want this only when unique is set to true, otherwise all those extra values will be equal to 1. When index is also set to true (see above), the multiplicity column is added after the row index. Useful when paginating over the sample in linear mode. Once you delete a sample, it is permanently deleted. If you try to delete a sample a second time, or a sample that does not exist, you will receive a "404 not found" response.
However, if you try to delete a sample that is being used at the moment, then BigML. To list all the samples, you can use the sample base URL. By default, only the 20 most recent samples will be returned. You can get your list of samples directly in your browser using your own username and API key with the following links. You can also paginate, filter, and order your samples. You can also list all of your correlations.
Example: "This is a description of my new correlation" discretization Object Global numeric field transformation parameters.