JavaScript seems to be disabled in your browser. You must have JavaScript enabled in your browser to utilize the functionality of this website. Sortiraj po: Pozicija Ime Cijena Najprodavaniji. T aluminijski profil 15 x 15 x 1,5 x mm, eloksi T aluminijski profil 20 x 20 x 1,5 x mm, eloksi T aluminijski profil 35 x 35 x 3 x mm, eloksira Kutni profil od mesinga 30 x 15 x 1,5 x mm.

Plosnati aluminijski profil 25 x 2 x mm, eloksi Plosnati profil od mesinga 7 x 2,5 x mm. Plosnati profil od mesinga 10 x 2 x mm. Plosnati profil od mesinga 20 x 2 x mm. PVC plosnati profil 20 x 2 x mm. PVC plosnati profil 25 x 2 x mm. PVC plosnati profil 30 x 3 x mm. Plosnati aluminijski profil 15 x 2 x mm, eloksi Plosnati aluminijski profil 20 x 2 x mm, eloksi Plosnati aluminijski profil 30 x 2 x mm, eloksi Plosnati aluminijski profil 40 x 3 x mm, eloksi Plosnati aluminijski profil 50 x 3 x mm, eloksi Plosnati aluminijski profil 60 x 3 x mm, eloksi Plosnati aluminijski profil 25 x 2 x mm.

Plosnati aluminijski profil 30 x 2 x mm. Plosnati aluminijski profil 40 x 2 x mm. Okrugla cijev od mesinga 2 x 0,3 x mm. Okrugla cijev od mesinga 4 x 0,5 x mm. Birajte po:.Imate prijedlog, prigovor ili reklamaciju? Molimo kontaktirajte nas e-mailom ili telefonom. Radno vrijeme pozivnog centra: od ponedjeljka do subote od 8 do 16 sati. Alati za Pex-Alu-Pex cijevi. Alati za varenje PPR cijevi. Izolacija za cijevi.

Bakrene cijevi za centralno grijanje. PPR cijevi za vodu. PP cijevi za kanalizaciju. Brtve za kanalizacijske cijevi. Alati i materijal za lemljenje bakrenih cijevi. Pex-Alu-Pex cijevi za vodu i grijanje. PEHD alkaten cijevi za vodu. PEHD cijev za vodu fi 32 mm 1"10 bara.

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PEHD cijev za vodu fi 63 mm 2"10 bara. PEHD cijev za vodu fi 32 mm 1"16 bara. Bakrena Cu cijev fi 35 x 1 mm - za centralno grijanje. Bakrena Cu cijev fi 15 x 1 mm - za centralno grijanje. Bakrena Cu cijev fi 22 x 1 mm - za centralno grijanje. Bakrena Cu cijev fi 42 x 1 mm - za centralno grijanje.

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I could go on much further but will simply state that this company is top notch, we would highly recommend their services, and thanks to them, our Iceland experience was a trip of a lifetime. Knowing nothing of Iceland and traveling with a 13-year-old, I contacted Nordic Visitor and asked them to help me. Their recommendations were on-target and they selected tour agencies who were reliable, on time, courteous, helpful, and everything you need to feel okay about traveling in a foreign country.

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Ligue 2 18:45 Le Havre - Reims 1.Imagine, for example, that you collect data in a hourly basis and want to create a dataset aggregrating data collected over the whole day. So you only need to send the new generated data each hour to BigML, create a source and a dataset for each one and then merge all the individual datasets into one at the end of the day.

We usually call datasets created in this way multi-datasets. You can merge multi-datasets too so basically you can grow a dataset as much as you want. The example below will construct a new dataset that is the concatenation of three other datasets. However, there can be cases where each dataset might come from a different source and therefore have different field ids. The first one would define the final dataset fields.

Those will be the default resulting fields, together with their datatypes and so on. Then we need to specify, for each of the remaining datasets in the list, a mapping from the "standard" fields to those in the corresponding dataset. In our example, we're saying that the fields of the second dataset to be used during the concatenation are "000023", "000024" and "00003a", which correspond to the final fields having them as keys.

In the case of the third dataset, the fields used will be "000023", "000004" and "00000f". The optypes of the paired fields should match, and for the case of categorical fields, be a proper subset. If a final field has optype text, however, all values are converted to strings.

The next request will create a multi-dataset sampling the two input datasets differently. Each entry maps fields in the first dataset to fieds in the dataset referenced by the key.

Setting this parameter to true for a dataset will return a dataset containing sequence of the out-of-bag instances instead of the sampled instances. See the Section on Sampling for more details. Each value is a number between 0 and 1 specifying the sample rate for the dataset.

Basically in those cases the flow that BigML. See examples below to create a multi-dataset model, a multi-dataset ensemble, and a multi-dataset evaluation.

We apply the term dataset transformations to the set of operations to create new modified versions of your original dataset or just transformations to abbreviate. Keep in mind that you can sample, filter and extend a dataset all at once in only one API request. Also when cloning a dataset, you can modify the names, labels, descriptions and preferred flags of its fields using a fields argument with entries for those fields you want to change.

See a description for all the arguments below. Dataset Cloning Arguments Argument TypeDescription category optional Integer The category that best describes the dataset. See the category codes for the complete list of categories. Example: "category": 1 description optional String A description of the dataset up to 8192 characters long.

Example: "description": "This is a description of my new dataset" fields optional Object Updates the names, labels, and descriptions of the fields in the new dataset. An entry keyed with the field id of the original dataset for each field that will be updated. Specifying a range of rows. As illustrated in the following example, it's possible to provide a list of input fields, selecting the fields from the filtered input dataset that will be created.

Filtering happens before field picking and, therefore, the row filter can use fields that won't end up in the cloned dataset.My business would not be moving in the right direction if it wasn't for Ordering Co. I love the flexibility of the software and its demonstrated ability to continually evolve and improve.

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Representative sampling assures that inferences and conclusions can reasonably extend from the sample to the population as a whole. An experimental study involves taking measurements of the system under study, manipulating the system, and then taking additional measurements using the same procedure to determine if the manipulation has modified the values of the measurements.

In contrast, an observational study does not involve experimental manipulation. Two main statistical methods are used in data analysis: descriptive statistics, which summarize data from a sample using indexes such as the mean or standard deviation, and inferential statistics, which draw conclusions from data that are subject to random variation (e. Inferences on mathematical statistics are made under the framework of probability theory, which deals with the analysis of random phenomena. A standard statistical procedure involves the test of the relationship between two statistical data sets, or a data set and synthetic data drawn from idealized model.

A hypothesis is proposed for the statistical relationship between the two data sets, and this is compared as an alternative to an idealized null hypothesis of no relationship between two data sets. Rejecting or disproving the null hypothesis is done using statistical tests that quantify the sense in which the null can be proven false, given the data that are used in the test.

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Working from a null hypothesis, two basic forms of error are recognized: Type I errors (null hypothesis is falsely rejected giving a "false positive") and Type II errors (null hypothesis fails to be rejected and an actual difference between populations is missed giving a "false negative"). Many of these errors are classified as random (noise) or systematic (bias), but other types of errors (e. The presence of missing data or censoring may result in biased estimates and specific techniques have been developed to address these problems.

Statistics can be said to have begun in ancient civilization, going back at least to the 5th century BC, but it was not until the 18th century that it started to draw more heavily from calculus and probability theory.

While many scientific investigations make use of data, statistics is concerned with the use of data in the context of uncertainty and decision making in the face of uncertainty. Mathematical techniques used for this include mathematical analysis, linear algebra, stochastic analysis, differential equations, and measure-theoretic probability theory.

Populations can be diverse topics such as "all persons living in a country" or "every atom composing a crystal". Ideally, statisticians compile data about the entire population (an operation called census). This may be organized by governmental statistical institutes. Descriptive statistics can be used to summarize the population data.Example: true You can also use curl to customize a new batch prediction. For example, to create a new batch prediction named "my batch prediction", that will not include a header, and will only output the field "000001" together with the confidence for each prediction.

Once a batch prediction has been successfully created it will have the following properties. Creating a batch prediction is a process that can take just a few seconds or a few hours depending on the size of the dataset used as input and on the workload of BigML's systems. The batch prediction goes through a number of states until its finished. Through the status field in the batch prediction you can determine when it has been fully processed.

Once you delete a batch prediction, it is permanently deleted. If you try to delete a batch prediction a second time, or a batch prediction that does not exist, you will receive a "404 not found" response. However, if you try to delete a batch prediction that is being used at the moment, then BigML. To list all the batch predictions, you can use the batchprediction base URL.

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By default, only the 20 most recent batch predictions will be returned. You can get your list of batch predictions directly in your browser using your own username and API key with the following links. You can also paginate, filter, and order your batch predictions.

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Batch Centroids Last Updated: Monday, 2017-10-30 10:31 A batch centroid provides an easy way to compute a centroid for each instance in a dataset in only one request. Batch centroids are created asynchronously. You can also list all of your batch centroids. You can easily create a new batch centroid using curl as follows. All the fields in the dataset Specifies the fields in the dataset to be considered to create the batch centroid.

Example: "my new batch centroid" newline optional String,default is "LF" The new line character that you want to get as line break in the generated csv file: "LF", "CRLF". For example, to create a new batch centroid named "my batch centroid", that will not include a header, and will only ouput the field "000001" together with the distance for each centroid. Once a batch centroid has been successfully created it will have the following properties.

Creating a batch centroid is a process that can take just a few seconds or a few hours depending on the size of the dataset used as input and on the workload of BigML's systems. The batch centroid goes through a number of states until its finished.

Through the status field in the batch centroid you can determine when it has been fully processed. Once you delete a batch centroid, it is permanently deleted.