SNIG | SNIG : Accelerated Large Sparse Neural Network Inference | GPU library

by dian-lun-lin C++ Version: Current License: No License

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kandi X-RAY | SNIG Summary

SNIG is a C++ library typically used in Hardware, GPU applications. SNIG has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

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Support

SNIG has a low active ecosystem.

It has 12 star(s) with 4 fork(s). There are 3 watchers for this library.

It had no major release in the last 6 months.

SNIG has no issues reported. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of SNIG is current.

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SNIG has no bugs reported.

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SNIG has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

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Community Discussions

Trending Discussions on SNIG

Forecasting using GARCH model in R

garchFit function in R: Multivariate data inputs require lhs for the formula

QUESTION

Forecasting using GARCH model in R

Asked 2019-Jan-21 at 00:58

df=structure(list(X = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), 
    json_data.time.updated = structure(1:41, .Label = c("Jan 19, 2019 15:18:00 UTC", 
    "Jan 19, 2019 15:19:00 UTC", "Jan 19, 2019 15:51:00 UTC", 
    "Jan 19, 2019 15:52:00 UTC", "Jan 19, 2019 15:54:00 UTC", 
    "Jan 19, 2019 15:55:00 UTC", "Jan 19, 2019 15:57:00 UTC", 
    "Jan 19, 2019 15:58:00 UTC", "Jan 19, 2019 16:00:00 UTC", 
    "Jan 19, 2019 16:01:00 UTC", "Jan 19, 2019 16:03:00 UTC", 
    "Jan 19, 2019 16:04:00 UTC", "Jan 19, 2019 16:06:00 UTC", 
    "Jan 19, 2019 16:07:00 UTC", "Jan 19, 2019 16:09:00 UTC", 
    "Jan 19, 2019 16:10:00 UTC", "Jan 19, 2019 16:12:00 UTC", 
    "Jan 19, 2019 16:13:00 UTC", "Jan 19, 2019 16:15:00 UTC", 
    "Jan 19, 2019 16:16:00 UTC", "Jan 19, 2019 16:18:00 UTC", 
    "Jan 19, 2019 16:19:00 UTC", "Jan 19, 2019 16:21:00 UTC", 
    "Jan 19, 2019 16:22:00 UTC", "Jan 19, 2019 16:24:00 UTC", 
    "Jan 19, 2019 16:25:00 UTC", "Jan 19, 2019 16:27:00 UTC", 
    "Jan 19, 2019 16:28:00 UTC", "Jan 19, 2019 16:30:00 UTC", 
    "Jan 19, 2019 16:31:00 UTC", "Jan 19, 2019 16:33:00 UTC", 
    "Jan 19, 2019 16:34:00 UTC", "Jan 19, 2019 16:36:00 UTC", 
    "Jan 19, 2019 16:37:00 UTC", "Jan 19, 2019 16:39:00 UTC", 
    "Jan 19, 2019 16:40:00 UTC", "Jan 19, 2019 16:42:00 UTC", 
    "Jan 19, 2019 16:43:00 UTC", "Jan 19, 2019 16:45:00 UTC", 
    "Jan 19, 2019 16:46:00 UTC", "Jan 19, 2019 16:48:00 UTC"), class = "factor"), 
    json_data.time.updatedISO = structure(1:41, .Label = c("2019-01-19T15:18:00+00:00", 
    "2019-01-19T15:19:00+00:00", "2019-01-19T15:51:00+00:00", 
    "2019-01-19T15:52:00+00:00", "2019-01-19T15:54:00+00:00", 
    "2019-01-19T15:55:00+00:00", "2019-01-19T15:57:00+00:00", 
    "2019-01-19T15:58:00+00:00", "2019-01-19T16:00:00+00:00", 
    "2019-01-19T16:01:00+00:00", "2019-01-19T16:03:00+00:00", 
    "2019-01-19T16:04:00+00:00", "2019-01-19T16:06:00+00:00", 
    "2019-01-19T16:07:00+00:00", "2019-01-19T16:09:00+00:00", 
    "2019-01-19T16:10:00+00:00", "2019-01-19T16:12:00+00:00", 
    "2019-01-19T16:13:00+00:00", "2019-01-19T16:15:00+00:00", 
    "2019-01-19T16:16:00+00:00", "2019-01-19T16:18:00+00:00", 
    "2019-01-19T16:19:00+00:00", "2019-01-19T16:21:00+00:00", 
    "2019-01-19T16:22:00+00:00", "2019-01-19T16:24:00+00:00", 
    "2019-01-19T16:25:00+00:00", "2019-01-19T16:27:00+00:00", 
    "2019-01-19T16:28:00+00:00", "2019-01-19T16:30:00+00:00", 
    "2019-01-19T16:31:00+00:00", "2019-01-19T16:33:00+00:00", 
    "2019-01-19T16:34:00+00:00", "2019-01-19T16:36:00+00:00", 
    "2019-01-19T16:37:00+00:00", "2019-01-19T16:39:00+00:00", 
    "2019-01-19T16:40:00+00:00", "2019-01-19T16:42:00+00:00", 
    "2019-01-19T16:43:00+00:00", "2019-01-19T16:45:00+00:00", 
    "2019-01-19T16:46:00+00:00", "2019-01-19T16:48:00+00:00"), class = "factor"), 
    json_data.time.updateduk = structure(1:41, .Label = c("Jan 19, 2019 at 15:18 GMT", 
    "Jan 19, 2019 at 15:19 GMT", "Jan 19, 2019 at 15:51 GMT", 
    "Jan 19, 2019 at 15:52 GMT", "Jan 19, 2019 at 15:54 GMT", 
    "Jan 19, 2019 at 15:55 GMT", "Jan 19, 2019 at 15:57 GMT", 
    "Jan 19, 2019 at 15:58 GMT", "Jan 19, 2019 at 16:00 GMT", 
    "Jan 19, 2019 at 16:01 GMT", "Jan 19, 2019 at 16:03 GMT", 
    "Jan 19, 2019 at 16:04 GMT", "Jan 19, 2019 at 16:06 GMT", 
    "Jan 19, 2019 at 16:07 GMT", "Jan 19, 2019 at 16:09 GMT", 
    "Jan 19, 2019 at 16:10 GMT", "Jan 19, 2019 at 16:12 GMT", 
    "Jan 19, 2019 at 16:13 GMT", "Jan 19, 2019 at 16:15 GMT", 
    "Jan 19, 2019 at 16:16 GMT", "Jan 19, 2019 at 16:18 GMT", 
    "Jan 19, 2019 at 16:19 GMT", "Jan 19, 2019 at 16:21 GMT", 
    "Jan 19, 2019 at 16:22 GMT", "Jan 19, 2019 at 16:24 GMT", 
    "Jan 19, 2019 at 16:25 GMT", "Jan 19, 2019 at 16:27 GMT", 
    "Jan 19, 2019 at 16:28 GMT", "Jan 19, 2019 at 16:30 GMT", 
    "Jan 19, 2019 at 16:31 GMT", "Jan 19, 2019 at 16:33 GMT", 
    "Jan 19, 2019 at 16:34 GMT", "Jan 19, 2019 at 16:36 GMT", 
    "Jan 19, 2019 at 16:37 GMT", "Jan 19, 2019 at 16:39 GMT", 
    "Jan 19, 2019 at 16:40 GMT", "Jan 19, 2019 at 16:42 GMT", 
    "Jan 19, 2019 at 16:43 GMT", "Jan 19, 2019 at 16:45 GMT", 
    "Jan 19, 2019 at 16:46 GMT", "Jan 19, 2019 at 16:48 GMT"), class = "factor"), 
    code = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L), .Label = "USD", class = "factor"), rate = structure(1:41, .Label = c("3,735.7750", 
    "3,735.9150", "3,736.9100", "3,735.3200", "3,736.7717", "3,736.0750", 
    "3,734.9600", "3,734.9117", "3,734.2833", "3,734.4950", "3,735.8533", 
    "3,736.1917", "3,735.5450", "3,735.5867", "3,736.0617", "3,736.3417", 
    "3,737.0633", "3,736.9583", "3,737.1667", "3,737.1433", "3,737.0583", 
    "3,736.9283", "3,737.6383", "3,737.5167", "3,737.9133", "3,738.7533", 
    "3,738.6767", "3,738.5767", "3,738.5917", "3,738.8867", "3,739.6333", 
    "3,739.9600", "3,739.3383", "3,739.9267", "3,739.3067", "3,739.5867", 
    "3,739.6567", "3,739.4267", "3,739.1500", "3,739.8817", "3,739.5550"
    ), class = "factor"), description = structure(c(1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "United States Dollar", class = "factor"), 
    rate_float = structure(1:41, .Label = c("3735.775", "3735.915", 
    "3736.91", "3735.32", "3736.7717", "3736.075", "3734.96", 
    "3734.9117", "3734.2833", "3734.495", "3735.8533", "3736.1917", 
    "3735.545", "3735.5867", "3736.0617", "3736.3417", "3737.0633", 
    "3736.9583", "3737.1667", "3737.1433", "3737.0583", "3736.9283", 
    "3737.6383", "3737.5167", "3737.9133", "3738.7533", "3738.6767", 
    "3738.5767", "3738.5917", "3738.8867", "3739.6333", "3739.96", 
    "3739.3383", "3739.9267", "3739.3067", "3739.5867", "3739.6567", 
    "3739.4267", "3739.15", "3739.8817", "3739.555"), class = "factor")), row.names = c(NA, 
41L), class = "data.frame")

...

ANSWER

Answered 2019-Jan-21 at 00:58

You may forecast 10 steps ahead with

Source https://stackoverflow.com/questions/54278313

QUESTION

garchFit function in R: Multivariate data inputs require lhs for the formula

Asked 2019-Jan-20 at 15:36

df=structure(list(X.1 = 1:10, X = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L), json_data.time.updated = structure(1:10, .Label = c("Jan 19, 2019 15:18:00 UTC", 
"Jan 19, 2019 15:19:00 UTC", "Jan 19, 2019 15:51:00 UTC", "Jan 19, 2019 15:52:00 UTC", 
"Jan 19, 2019 15:54:00 UTC", "Jan 19, 2019 15:55:00 UTC", "Jan 19, 2019 15:57:00 UTC", 
"Jan 19, 2019 15:58:00 UTC", "Jan 19, 2019 16:00:00 UTC", "Jan 19, 2019 16:01:00 UTC"
), class = "factor"), json_data.time.updatedISO = structure(1:10, .Label = c("2019-01-19T15:18:00+00:00", 
"2019-01-19T15:19:00+00:00", "2019-01-19T15:51:00+00:00", "2019-01-19T15:52:00+00:00", 
"2019-01-19T15:54:00+00:00", "2019-01-19T15:55:00+00:00", "2019-01-19T15:57:00+00:00", 
"2019-01-19T15:58:00+00:00", "2019-01-19T16:00:00+00:00", "2019-01-19T16:01:00+00:00"
), class = "factor"), json_data.time.updateduk = structure(1:10, .Label = c("Jan 19, 2019 at 15:18 GMT", 
"Jan 19, 2019 at 15:19 GMT", "Jan 19, 2019 at 15:51 GMT", "Jan 19, 2019 at 15:52 GMT", 
"Jan 19, 2019 at 15:54 GMT", "Jan 19, 2019 at 15:55 GMT", "Jan 19, 2019 at 15:57 GMT", 
"Jan 19, 2019 at 15:58 GMT", "Jan 19, 2019 at 16:00 GMT", "Jan 19, 2019 at 16:01 GMT"
), class = "factor"), code = structure(c(1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L), .Label = "USD", class = "factor"), rate = structure(c(6L, 
7L, 10L, 5L, 9L, 8L, 4L, 3L, 1L, 2L), .Label = c("3,734.2833", 
"3,734.4950", "3,734.9117", "3,734.9600", "3,735.3200", "3,735.7750", 
"3,735.9150", "3,736.0750", "3,736.7717", "3,736.9100"), class = "factor"), 
    description = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L), .Label = "United States Dollar", class = "factor"), 
    rate_float = c(3735.775, 3735.915, 3736.91, 3735.32, 3736.7717, 
    3736.075, 3734.96, 3734.9117, 3734.2833, 3734.495)), class = "data.frame", row.names = c(NA, 
-10L))

...

ANSWER

Answered 2019-Jan-20 at 14:59

You specified data = df, where df has multiple columns, while the model is just ~ garch(1, 1), so there is no way to know which of the variables is supposed to follow this GARCH(1,1). Hence, the errors says that then you need to specify the left hand side. For instance, using

Source https://stackoverflow.com/questions/54277432

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install SNIG

You can download the dataset either by yourself or by our script.
Check ~$ ./get_dataset.sh -h for more details. Note that this script may fail to get the dataset due to various environment.
The dataset is available at https://graphchallenge.mit.edu/data-sets.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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