Bus Mall Marketing Solutions Operations Survey
INSTRUCTIONS: Read this FIRST! You will be given 25 different product image selections, choose the product image you would most likely buy.


Main lines of the network and nodes
Line width - Type of line (single, double, triple...)
Line color - Transmission power of the line
Node color - Type of node


Class: Aws::MachineLearning::Types::S3DataSpec
| Relationships & Source Files | |
| Super Chains via Extension / Inclusion / Inheritance | |
|
Class Chain:
self,
Struct
|
|
|
Instance Chain:
self,
::Aws::Structure ,
Struct
|
|
| Inherits: |
Struct
|
| Defined in: | gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/types.rb |
Overview
Describes the data specification of a DataSource
.
Constant Summary
Instance Attribute Summary
-
#data_location_s3 ⇒ String
rw
The location of the data file(s) used by a
DataSource
. -
#data_rearrangement ⇒ String
rw
A JSON string that represents the splitting and rearrangement processing to be applied to a
DataSource
. -
#data_schema ⇒ String
rw
A JSON string that represents the schema for an Amazon S3
DataSource
. -
#data_schema_location_s3 ⇒ String
rw
Describes the schema location in Amazon S3.
Instance Method Summary
::Aws::Structure
- Included
| #to_hash | Alias for Structure#to_h. |
Instance Attribute Details
#data_location_s3 ⇒ String
(rw)
The location of the data file(s) used by a DataSource
. The URI
specifies a data file or an Amazon Simple Storage Service (Amazon
::Aws::S3
) directory or bucket containing data files.
# File 'gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/types.rb', line 3506
class S3DataSpec < Struct.new( :data_location_s3, :data_rearrangement, :data_schema, :data_schema_location_s3) SENSITIVE = [] include Aws::Structure end
#data_rearrangement ⇒ String
(rw)
A JSON string that represents the splitting and rearrangement
processing to be applied to a DataSource
. If the
DataRearrangement
parameter is not provided, all of the input data
is used to create the Datasource
.
There are multiple parameters that control what data is used to create a datasource:
-
percentBegin
Use percentBegin
to indicate the beginning of the range of the
data used to create the Datasource. If you do not include
percentBegin
and percentEnd
, Amazon ML includes all of the
data when creating the datasource.
-
percentEnd
Use percentEnd
to indicate the end of the range of the data used
to create the Datasource. If you do not include percentBegin
and
percentEnd
, Amazon ML includes all of the data when creating the
datasource.
-
complement
The complement
parameter instructs Amazon ML to use the data
that is not included in the range of percentBegin
to
percentEnd
to create a datasource. The complement
parameter is
useful if you need to create complementary datasources for
training and evaluation. To create a complementary datasource, use
the same values for percentBegin
and percentEnd
, along with
the complement
parameter.
For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.
Datasource for evaluation: \{"splitting":\{"percentBegin":0,
"percentEnd":25\}\}
Datasource for training: \{"splitting":\{"percentBegin":0,
"percentEnd":25, "complement":"true"\}\}
-
strategy
To change how Amazon ML splits the data for a datasource, use the
strategy
parameter.
The default value for the strategy
parameter is sequential
,
meaning that Amazon ML takes all of the data records between the
percentBegin
and percentEnd
parameters for the datasource, in
the order that the records appear in the input data.
The following two DataRearrangement
lines are examples of
sequentially ordered training and evaluation datasources:
Datasource for evaluation: \{"splitting":\{"percentBegin":70,
"percentEnd":100, "strategy":"sequential"\}\}
Datasource for training: \{"splitting":\{"percentBegin":70,
"percentEnd":100, "strategy":"sequential",
"complement":"true"\}\}
To randomly split the input data into the proportions indicated by
the percentBegin and percentEnd parameters, set the strategy
parameter to random
and provide a string that is used as the
seed value for the random data splitting (for example, you can use
the S3 path to your data as the random seed string). If you choose
the random split strategy, Amazon ML assigns each row of data a
pseudo-random number between 0 and 100, and then selects the rows
that have an assigned number between percentBegin
and
percentEnd
. Pseudo-random numbers are assigned using both the
input seed string value and the byte offset as a seed, so changing
the data results in a different split. Any existing ordering is
preserved. The random splitting strategy ensures that variables in
the training and evaluation data are distributed similarly. It is
useful in the cases where the input data may have an implicit sort
order, which would otherwise result in training and evaluation
datasources containing non-similar data records.
The following two DataRearrangement
lines are examples of
non-sequentially ordered training and evaluation datasources:
Datasource for evaluation: \{"splitting":\{"percentBegin":70,
"percentEnd":100, "strategy":"random",
"randomSeed"="s3://my_s3_path/bucket/file.csv"\}\}
Datasource for training: \{"splitting":\{"percentBegin":70,
"percentEnd":100, "strategy":"random",
"randomSeed"="s3://my_s3_path/bucket/file.csv",
"complement":"true"\}\}
# File 'gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/types.rb', line 3506
class S3DataSpec < Struct.new( :data_location_s3, :data_rearrangement, :data_schema, :data_schema_location_s3) SENSITIVE = [] include Aws::Structure end
#data_schema ⇒ String
(rw)
A JSON string that represents the schema for an Amazon S3
DataSource
. The DataSchema
defines the structure of the
observation data in the data file(s) referenced in the DataSource
.
You must provide either the DataSchema
or the
DataSchemaLocationS3
.
Define your DataSchema
as a series of key-value pairs.
attributes
and excludedVariableNames
have an array of key-value
pairs for their value. Use the following format to define your
DataSchema
.
\{ "version": "1.0",
"recordAnnotationFieldName": "F1",
"recordWeightFieldName": "F2",
"targetFieldName": "F3",
"dataFormat": "CSV",
"dataFileContainsHeader": true,
"attributes": [
\{ "fieldName": "F1", "fieldType": "TEXT" \}, \{ "fieldName": "F2", "fieldType": "NUMERIC" \}, \{ "fieldName": "F3", "fieldType": "CATEGORICAL" \}, \{ "fieldName": "F4", "fieldType": "NUMERIC" \}, \{ "fieldName": "F5", "fieldType": "CATEGORICAL" \}, \{ "fieldName": "F6", "fieldType": "TEXT" \}, \{ "fieldName": "F7", "fieldType": "WEIGHTED_INT_SEQUENCE" \}, \{ "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE" \} ],
"excludedVariableNames": [ "F6" ] \}
# File 'gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/types.rb', line 3506
class S3DataSpec < Struct.new( :data_location_s3, :data_rearrangement, :data_schema, :data_schema_location_s3) SENSITIVE = [] include Aws::Structure end
#data_schema_location_s3 ⇒ String
(rw)
Describes the schema location in Amazon S3. You must provide either
the DataSchema
or the DataSchemaLocationS3
.
# File 'gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/types.rb', line 3506
class S3DataSpec < Struct.new( :data_location_s3, :data_rearrangement, :data_schema, :data_schema_location_s3) SENSITIVE = [] include Aws::Structure end


{% extends "accounts/account_form.html" %} {% load i18n %} {% block extra_head %} {% load static %} {% endblock %} {% block main %}
{% trans "Enter your username or email address and you'll receive an email with a link you need to click, in order to log in and change your password." %}


Quantum Computing, for Humans.
Read our documentation here:
Documentation
Copyright © 2021 qbitkit Team · All Rights Reserved


Dear Sir/Madam, I am Joseph Dubia from the Republic of Sierra-leone. As I write to you now I am in possession of $3,500,000ud (three million five hundred thousand U.S dollers) brought in by International Red Cross Organisation into Ghana, which is where i am taking political asylum as a refugee under the protection of the United Nations High Commissioner of the Refugees. The assistance I need from you is this, the Dollars I brought from my country is the old notes of 50 dollar bills. This old dollar bills is no more acceptable over here in Africa, except in Europe, Asia and America. I need your assistance to help me ship the fund abroad where it will be useful for investment purpose and to assist me further my education abroad. Attached to this mail is one piece of the dollar. Sir, the International Red Cross Organisation has promised to assist me make the delivery to any of my desired location.I will send you my UN Refugee identity Card for you to know whom you are dealing with.But that will be after I recieve your response. Should my appeal meet your pleasure,I will be very glad as I await your swift response. Yours Sincerely, Joseph Dubia.
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Identity QuantConnect.Algorithm.QCAlgorithm.Identity (Symbol
symbol,*Func<IBaseData, Decimal>
selector,*String
fieldName )
Creates a new Identity indicator for the symbol The indicator will be automatically updated on the symbol's subscription resolution.
Identity QuantConnect.Algorithm.QCAlgorithm.Identity (Symbol
symbol,Resolution
resolution,*Func<IBaseData, Decimal>
selector,*String
fieldName )
Creates a new Identity indicator for the symbol The indicator will be automatically updated on the symbol's subscription resolution.
Identity QuantConnect.Algorithm.QCAlgorithm.Identity (Symbol
symbol,TimeSpan
resolution,*Func<IBaseData, Decimal>
selector,*String
fieldName )
Creates a new Identity indicator for the symbol The indicator will be automatically updated on the symbol's subscription resolution.


Before Change
// time.sleep(10.0)
for m_thread in self._tf_records_writer_threads:
m_thread.start()
self._sample_info_queue.join()
self._sample_feature_io_queue.join()
After Change
log.info("Start write tensorflow records for {:s}...".format(self._dataset_flag))
process_pool = []
tfwriters = []
for i in range(self._writer_process_nums):
tfrecords_save_name = "{:s}_{:d}.tfrecords".format(self._dataset_flag, i + 1)
tfrecords_save_path = ops.join(self._tfrecords_save_dir, tfrecords_save_name)
tfrecords_io_writer = tf.python_io.TFRecordWriter(path=tfrecords_save_path)
process = Process(
target=_write_tfrecords,
name="Subprocess_{:d}".format(i + 1),
args=(tfrecords_io_writer,)
)
process_pool.append(process)
tfwriters.append(tfrecords_io_writer)
process.start()
for process in process_pool:
process.join()
log.info("Finished writing down the tensorflow records file")


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This collection of examples has been distributed with the Monash University Version of clp(R) [Heintze et al. 87], and its inclusion into this distribution was kindly permitted by Roland Yap.
Assuming you are using clp(R):
clp(r) ?- [library('clpqr/examples/monash/rkf45')]. clp(r) ?- go. Point 0.00000 : 0.75000 0.00000 Point 0.50000 : 0.61969 0.47793 Point 1.00000 : 0.29417 0.81233 Point 1.50000 : -0.10556 0.95809 Point 2.00000 : -0.49076 0.93977 Point 2.50000 : -0.81440 0.79929 Point 3.00000 : -1.05440 0.57522 Iteration finished ------------------ 439 derivative evaluations