Add FM and LDA to the documentation.#66

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lukmis merged 2 commits into from
Feb 6, 2018
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Add FM and LDA to the documentation. #66

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Add FM and LDA to the documentation.
lukmis Jan 31, 2018
b5681fa
Merge branch 'master' into docs_fix
yangaws Feb 5, 2018
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6 changes: 3 additions & 3 deletions README.rst
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -39,7 +39,7 @@ You can install from source by cloning this repository and issuing a pip install

git clone https://github.com/aws/sagemaker-python-sdk.git
python setup.py sdist
pip install dist/sagemaker-1.0.0.tar.gz
pip install dist/sagemaker-1.0.3.tar.gz

Supported Python versions
~~~~~~~~~~~~~~~~~~~~~~~~~
Expand DownExpand Up@@ -1447,11 +1447,11 @@ Amazon SageMaker provides several built-in machine learning algorithms that you

The full list of algorithms is available on the AWS website: https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html

SageMaker Python SDK includes Estimator wrappers for the AWS K-means, Principal Components Analysis, and Linear Learner algorithms.
SageMaker Python SDK includes Estimator wrappers for the AWS K-means, Principal Components Analysis, Linear Learner, Factorization Machines and LDA algorithms.

Definition and usage
~~~~~~~~~~~~~~~~~~~~
Estimators that wrap Amazon's built-in algorithms define algorithm's hyperparameters with defaults. When a default is not possible you need to provide the value during construction:
Estimators that wrap Amazon's built-in algorithms define algorithm's hyperparameters with defaults. When a default is not possible you need to provide the value during construction, e.g.:

- ``KMeans`` Estimator requires parameter ``k`` to define number of clusters
- ``PCA`` Estimator requires parameter ``num_components`` to define number of principal components
Expand Down
2 changes: 1 addition & 1 deletion doc/conf.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -18,7 +18,7 @@ def __getattr__(cls, name):
'tensorflow.python.framework', 'tensorflow_serving', 'tensorflow_serving.apis']
sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES)

version = '1.0'
version = '1.0.3'
project = u'sagemaker'

# Add any Sphinx extension module names here, as strings. They can be extensions
Expand Down
22 changes: 22 additions & 0 deletions doc/factorization_machines.rst
View file
Open in desktop
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,22 @@
FactorizationMachines
-------------------------

The Amazon SageMaker Factorization Machines algorithm.

.. autoclass:: sagemaker.FactorizationMachines
:members:
:undoc-members:
:show-inheritance:
:inherited-members:
:exclude-members: image, num_factors, predictor_type, epochs, clip_gradient, mini_batch_size, feature_dim, eps, rescale_grad, bias_lr, linear_lr, factors_lr, bias_wd, linear_wd, factors_wd, bias_init_method, bias_init_scale, bias_init_sigma, bias_init_value, linear_init_method, linear_init_scale, linear_init_sigma, linear_init_value, factors_init_method, factors_init_scale, factors_init_sigma, factors_init_value


.. autoclass:: sagemaker.FactorizationMachinesModel
:members:
:undoc-members:
:show-inheritance:

.. autoclass:: sagemaker.FactorizationMachinesPredictor
:members:
:undoc-members:
:show-inheritance:
5 changes: 4 additions & 1 deletion doc/index.rst
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -38,11 +38,14 @@ A managed environment for TensorFlow training and hosting on Amazon SageMaker

SageMaker First-Party Algorithms
--------------------------------
Amazon provides implementations of some common machine learning algortithms optimized for GPU archicture and massive datasets.
Amazon provides implementations of some common machine learning algortithms optimized for GPU architecture and massive datasets.

.. toctree::
:maxdepth: 2

kmeans
pca
linear_learner
sagemaker.amazon.amazon_estimator
factorization_machines
lda
22 changes: 22 additions & 0 deletions doc/lda.rst
View file
Open in desktop
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,22 @@
LDA
--------------------

The Amazon SageMaker LDA algorithm.

.. autoclass:: sagemaker.LDA
:members:
:undoc-members:
:show-inheritance:
:inherited-members:
:exclude-members: image, num_topics, alpha0, max_restarts, max_iterations, mini_batch_size, feature_dim, tol


.. autoclass:: sagemaker.LDAModel
:members:
:undoc-members:
:show-inheritance:

.. autoclass:: sagemaker.LDAPredictor
:members:
:undoc-members:
:show-inheritance:
, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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Add FM and LDA to the documentation.#66

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 Merged

lukmis
merged 2 commits into

from

Feb 6, 2018
 Merged


Add FM and LDA to the documentation.
#66
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e3a8818

Add FM and LDA to the documentation.


lukmis Jan 31, 2018
b5681fa

Merge branch 'master' into docs_fix


yangaws Feb 5, 2018

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6 changes: 3 additions & 3 deletions
README.rst

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -39,7 +39,7 @@ You can install from source by cloning this repository and issuing a pip install

 git clone https://github.com/aws/sagemaker-python-sdk.git
 python setup.py sdist
 pip install dist/sagemaker-1.0.0.tar.gz
 pip install dist/sagemaker-1.0.3.tar.gz

Supported Python versions
~~~~~~~~~~~~~~~~~~~~~~~~~
Expand DownExpand Up@@ -1447,11 +1447,11 @@ Amazon SageMaker provides several built-in machine learning algorithms that you

The full list of algorithms is available on the AWS website: https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html

SageMaker Python SDK includes Estimator wrappers for the AWS K-means, Principal Components Analysis, and Linear Learner algorithms.
SageMaker Python SDK includes Estimator wrappers for the AWS K-means, Principal Components Analysis, Linear Learner, Factorization Machines and LDA algorithms.

Definition and usage
~~~~~~~~~~~~~~~~~~~~
Estimators that wrap Amazon's built-in algorithms define algorithm's hyperparameters with defaults. When a default is not possible you need to provide the value during construction:
Estimators that wrap Amazon's built-in algorithms define algorithm's hyperparameters with defaults. When a default is not possible you need to provide the value during construction, e.g.:

- ``KMeans`` Estimator requires parameter ``k`` to define number of clusters
- ``PCA`` Estimator requires parameter ``num_components`` to define number of principal components
Expand Down

2 changes: 1 addition & 1 deletion
doc/conf.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -18,7 +18,7 @@ def __getattr__(cls, name):
 'tensorflow.python.framework', 'tensorflow_serving', 'tensorflow_serving.apis']
sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES)

version = '1.0'
version = '1.0.3'
project = u'sagemaker'

# Add any Sphinx extension module names here, as strings. They can be extensions
Expand Down

22 changes: 22 additions & 0 deletions
doc/factorization_machines.rst

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,22 @@
FactorizationMachines
-------------------------

The Amazon SageMaker Factorization Machines algorithm.

.. autoclass:: sagemaker.FactorizationMachines
 :members:
 :undoc-members:
 :show-inheritance:
 :inherited-members:
 :exclude-members: image, num_factors, predictor_type, epochs, clip_gradient, mini_batch_size, feature_dim, eps, rescale_grad, bias_lr, linear_lr, factors_lr, bias_wd, linear_wd, factors_wd, bias_init_method, bias_init_scale, bias_init_sigma, bias_init_value, linear_init_method, linear_init_scale, linear_init_sigma, linear_init_value, factors_init_method, factors_init_scale, factors_init_sigma, factors_init_value


.. autoclass:: sagemaker.FactorizationMachinesModel
 :members:
 :undoc-members:
 :show-inheritance:

.. autoclass:: sagemaker.FactorizationMachinesPredictor
 :members:
 :undoc-members:
 :show-inheritance:

5 changes: 4 additions & 1 deletion
doc/index.rst

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -38,11 +38,14 @@ A managed environment for TensorFlow training and hosting on Amazon SageMaker

SageMaker First-Party Algorithms
--------------------------------
Amazon provides implementations of some common machine learning algortithms optimized for GPU archicture and massive datasets.
Amazon provides implementations of some common machine learning algortithms optimized for GPU architecture and massive datasets.

.. toctree::
 :maxdepth: 2

 kmeans
 pca
 linear_learner
 sagemaker.amazon.amazon_estimator
 factorization_machines
 lda

22 changes: 22 additions & 0 deletions
doc/lda.rst

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,22 @@
LDA
--------------------

The Amazon SageMaker LDA algorithm.

.. autoclass:: sagemaker.LDA
 :members:
 :undoc-members:
 :show-inheritance:
 :inherited-members:
 :exclude-members: image, num_topics, alpha0, max_restarts, max_iterations, mini_batch_size, feature_dim, tol


.. autoclass:: sagemaker.LDAModel
 :members:
 :undoc-members:
 :show-inheritance:

.. autoclass:: sagemaker.LDAPredictor
 :members:
 :undoc-members:
 :show-inheritance:

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
Add FM and LDA to the documentation.#66

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 Sign up for GitHub

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Already on GitHub?
Sign in
to your account

 Merged

lukmis
merged 2 commits into

from

Feb 6, 2018
 Merged


Add FM and LDA to the documentation.
#66
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e3a8818

Add FM and LDA to the documentation.


lukmis Jan 31, 2018
b5681fa

Merge branch 'master' into docs_fix


yangaws Feb 5, 2018

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6 changes: 3 additions & 3 deletions
README.rst

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -39,7 +39,7 @@ You can install from source by cloning this repository and issuing a pip install

 git clone https://github.com/aws/sagemaker-python-sdk.git
 python setup.py sdist
 pip install dist/sagemaker-1.0.0.tar.gz
 pip install dist/sagemaker-1.0.3.tar.gz

Supported Python versions
~~~~~~~~~~~~~~~~~~~~~~~~~
Expand DownExpand Up@@ -1447,11 +1447,11 @@ Amazon SageMaker provides several built-in machine learning algorithms that you

The full list of algorithms is available on the AWS website: https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html

SageMaker Python SDK includes Estimator wrappers for the AWS K-means, Principal Components Analysis, and Linear Learner algorithms.
SageMaker Python SDK includes Estimator wrappers for the AWS K-means, Principal Components Analysis, Linear Learner, Factorization Machines and LDA algorithms.

Definition and usage
~~~~~~~~~~~~~~~~~~~~
Estimators that wrap Amazon's built-in algorithms define algorithm's hyperparameters with defaults. When a default is not possible you need to provide the value during construction:
Estimators that wrap Amazon's built-in algorithms define algorithm's hyperparameters with defaults. When a default is not possible you need to provide the value during construction, e.g.:

- ``KMeans`` Estimator requires parameter ``k`` to define number of clusters
- ``PCA`` Estimator requires parameter ``num_components`` to define number of principal components
Expand Down

2 changes: 1 addition & 1 deletion
doc/conf.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -18,7 +18,7 @@ def __getattr__(cls, name):
 'tensorflow.python.framework', 'tensorflow_serving', 'tensorflow_serving.apis']
sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES)

version = '1.0'
version = '1.0.3'
project = u'sagemaker'

# Add any Sphinx extension module names here, as strings. They can be extensions
Expand Down

22 changes: 22 additions & 0 deletions
doc/factorization_machines.rst

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,22 @@
FactorizationMachines
-------------------------

The Amazon SageMaker Factorization Machines algorithm.

.. autoclass:: sagemaker.FactorizationMachines
 :members:
 :undoc-members:
 :show-inheritance:
 :inherited-members:
 :exclude-members: image, num_factors, predictor_type, epochs, clip_gradient, mini_batch_size, feature_dim, eps, rescale_grad, bias_lr, linear_lr, factors_lr, bias_wd, linear_wd, factors_wd, bias_init_method, bias_init_scale, bias_init_sigma, bias_init_value, linear_init_method, linear_init_scale, linear_init_sigma, linear_init_value, factors_init_method, factors_init_scale, factors_init_sigma, factors_init_value


.. autoclass:: sagemaker.FactorizationMachinesModel
 :members:
 :undoc-members:
 :show-inheritance:

.. autoclass:: sagemaker.FactorizationMachinesPredictor
 :members:
 :undoc-members:
 :show-inheritance:

5 changes: 4 additions & 1 deletion
doc/index.rst

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -38,11 +38,14 @@ A managed environment for TensorFlow training and hosting on Amazon SageMaker

SageMaker First-Party Algorithms
--------------------------------
Amazon provides implementations of some common machine learning algortithms optimized for GPU archicture and massive datasets.
Amazon provides implementations of some common machine learning algortithms optimized for GPU architecture and massive datasets.

.. toctree::
 :maxdepth: 2

 kmeans
 pca
 linear_learner
 sagemaker.amazon.amazon_estimator
 factorization_machines
 lda

22 changes: 22 additions & 0 deletions
doc/lda.rst

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,22 @@
LDA
--------------------

The Amazon SageMaker LDA algorithm.

.. autoclass:: sagemaker.LDA
 :members:
 :undoc-members:
 :show-inheritance:
 :inherited-members:
 :exclude-members: image, num_topics, alpha0, max_restarts, max_iterations, mini_batch_size, feature_dim, tol


.. autoclass:: sagemaker.LDAModel
 :members:
 :undoc-members:
 :show-inheritance:

.. autoclass:: sagemaker.LDAPredictor
 :members:
 :undoc-members:
 :show-inheritance:

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
Add FM and LDA to the documentation.#66

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Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

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Already on GitHub?
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to your account

 Merged

lukmis
merged 2 commits into

from

Feb 6, 2018
 Merged


Add FM and LDA to the documentation.
#66
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e3a8818

Add FM and LDA to the documentation.


lukmis Jan 31, 2018
b5681fa

Merge branch 'master' into docs_fix


yangaws Feb 5, 2018

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6 changes: 3 additions & 3 deletions
README.rst

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -39,7 +39,7 @@ You can install from source by cloning this repository and issuing a pip install

 git clone https://github.com/aws/sagemaker-python-sdk.git
 python setup.py sdist
 pip install dist/sagemaker-1.0.0.tar.gz
 pip install dist/sagemaker-1.0.3.tar.gz

Supported Python versions
~~~~~~~~~~~~~~~~~~~~~~~~~
Expand DownExpand Up@@ -1447,11 +1447,11 @@ Amazon SageMaker provides several built-in machine learning algorithms that you

The full list of algorithms is available on the AWS website: https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html

SageMaker Python SDK includes Estimator wrappers for the AWS K-means, Principal Components Analysis, and Linear Learner algorithms.
SageMaker Python SDK includes Estimator wrappers for the AWS K-means, Principal Components Analysis, Linear Learner, Factorization Machines and LDA algorithms.

Definition and usage
~~~~~~~~~~~~~~~~~~~~
Estimators that wrap Amazon's built-in algorithms define algorithm's hyperparameters with defaults. When a default is not possible you need to provide the value during construction:
Estimators that wrap Amazon's built-in algorithms define algorithm's hyperparameters with defaults. When a default is not possible you need to provide the value during construction, e.g.:

- ``KMeans`` Estimator requires parameter ``k`` to define number of clusters
- ``PCA`` Estimator requires parameter ``num_components`` to define number of principal components
Expand Down

2 changes: 1 addition & 1 deletion
doc/conf.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -18,7 +18,7 @@ def __getattr__(cls, name):
 'tensorflow.python.framework', 'tensorflow_serving', 'tensorflow_serving.apis']
sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES)

version = '1.0'
version = '1.0.3'
project = u'sagemaker'

# Add any Sphinx extension module names here, as strings. They can be extensions
Expand Down

22 changes: 22 additions & 0 deletions
doc/factorization_machines.rst

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,22 @@
FactorizationMachines
-------------------------

The Amazon SageMaker Factorization Machines algorithm.

.. autoclass:: sagemaker.FactorizationMachines
 :members:
 :undoc-members:
 :show-inheritance:
 :inherited-members:
 :exclude-members: image, num_factors, predictor_type, epochs, clip_gradient, mini_batch_size, feature_dim, eps, rescale_grad, bias_lr, linear_lr, factors_lr, bias_wd, linear_wd, factors_wd, bias_init_method, bias_init_scale, bias_init_sigma, bias_init_value, linear_init_method, linear_init_scale, linear_init_sigma, linear_init_value, factors_init_method, factors_init_scale, factors_init_sigma, factors_init_value


.. autoclass:: sagemaker.FactorizationMachinesModel
 :members:
 :undoc-members:
 :show-inheritance:

.. autoclass:: sagemaker.FactorizationMachinesPredictor
 :members:
 :undoc-members:
 :show-inheritance:

5 changes: 4 additions & 1 deletion
doc/index.rst

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -38,11 +38,14 @@ A managed environment for TensorFlow training and hosting on Amazon SageMaker

SageMaker First-Party Algorithms
--------------------------------
Amazon provides implementations of some common machine learning algortithms optimized for GPU archicture and massive datasets.
Amazon provides implementations of some common machine learning algortithms optimized for GPU architecture and massive datasets.

.. toctree::
 :maxdepth: 2

 kmeans
 pca
 linear_learner
 sagemaker.amazon.amazon_estimator
 factorization_machines
 lda

22 changes: 22 additions & 0 deletions
doc/lda.rst

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,22 @@
LDA
--------------------

The Amazon SageMaker LDA algorithm.

.. autoclass:: sagemaker.LDA
 :members:
 :undoc-members:
 :show-inheritance:
 :inherited-members:
 :exclude-members: image, num_topics, alpha0, max_restarts, max_iterations, mini_batch_size, feature_dim, tol


.. autoclass:: sagemaker.LDAModel
 :members:
 :undoc-members:
 :show-inheritance:

.. autoclass:: sagemaker.LDAPredictor
 :members:
 :undoc-members:
 :show-inheritance:

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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lukmis
merged 2 commits into

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Feb 6, 2018
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Add FM and LDA to the documentation.
#66
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e3a8818

Add FM and LDA to the documentation.


lukmis Jan 31, 2018
b5681fa

Merge branch 'master' into docs_fix


yangaws Feb 5, 2018

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6 changes: 3 additions & 3 deletions
README.rst

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -39,7 +39,7 @@ You can install from source by cloning this repository and issuing a pip install

 git clone https://github.com/aws/sagemaker-python-sdk.git
 python setup.py sdist
 pip install dist/sagemaker-1.0.0.tar.gz
 pip install dist/sagemaker-1.0.3.tar.gz

Supported Python versions
~~~~~~~~~~~~~~~~~~~~~~~~~
Expand DownExpand Up@@ -1447,11 +1447,11 @@ Amazon SageMaker provides several built-in machine learning algorithms that you

The full list of algorithms is available on the AWS website: https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html

SageMaker Python SDK includes Estimator wrappers for the AWS K-means, Principal Components Analysis, and Linear Learner algorithms.
SageMaker Python SDK includes Estimator wrappers for the AWS K-means, Principal Components Analysis, Linear Learner, Factorization Machines and LDA algorithms.

Definition and usage
~~~~~~~~~~~~~~~~~~~~
Estimators that wrap Amazon's built-in algorithms define algorithm's hyperparameters with defaults. When a default is not possible you need to provide the value during construction:
Estimators that wrap Amazon's built-in algorithms define algorithm's hyperparameters with defaults. When a default is not possible you need to provide the value during construction, e.g.:

- ``KMeans`` Estimator requires parameter ``k`` to define number of clusters
- ``PCA`` Estimator requires parameter ``num_components`` to define number of principal components
Expand Down

2 changes: 1 addition & 1 deletion
doc/conf.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -18,7 +18,7 @@ def __getattr__(cls, name):
 'tensorflow.python.framework', 'tensorflow_serving', 'tensorflow_serving.apis']
sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES)

version = '1.0'
version = '1.0.3'
project = u'sagemaker'

# Add any Sphinx extension module names here, as strings. They can be extensions
Expand Down

22 changes: 22 additions & 0 deletions
doc/factorization_machines.rst

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,22 @@
FactorizationMachines
-------------------------

The Amazon SageMaker Factorization Machines algorithm.

.. autoclass:: sagemaker.FactorizationMachines
 :members:
 :undoc-members:
 :show-inheritance:
 :inherited-members:
 :exclude-members: image, num_factors, predictor_type, epochs, clip_gradient, mini_batch_size, feature_dim, eps, rescale_grad, bias_lr, linear_lr, factors_lr, bias_wd, linear_wd, factors_wd, bias_init_method, bias_init_scale, bias_init_sigma, bias_init_value, linear_init_method, linear_init_scale, linear_init_sigma, linear_init_value, factors_init_method, factors_init_scale, factors_init_sigma, factors_init_value


.. autoclass:: sagemaker.FactorizationMachinesModel
 :members:
 :undoc-members:
 :show-inheritance:

.. autoclass:: sagemaker.FactorizationMachinesPredictor
 :members:
 :undoc-members:
 :show-inheritance:

5 changes: 4 additions & 1 deletion
doc/index.rst

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -38,11 +38,14 @@ A managed environment for TensorFlow training and hosting on Amazon SageMaker

SageMaker First-Party Algorithms
--------------------------------
Amazon provides implementations of some common machine learning algortithms optimized for GPU archicture and massive datasets.
Amazon provides implementations of some common machine learning algortithms optimized for GPU architecture and massive datasets.

.. toctree::
 :maxdepth: 2

 kmeans
 pca
 linear_learner
 sagemaker.amazon.amazon_estimator
 factorization_machines
 lda

22 changes: 22 additions & 0 deletions
doc/lda.rst

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,22 @@
LDA
--------------------

The Amazon SageMaker LDA algorithm.

.. autoclass:: sagemaker.LDA
 :members:
 :undoc-members:
 :show-inheritance:
 :inherited-members:
 :exclude-members: image, num_topics, alpha0, max_restarts, max_iterations, mini_batch_size, feature_dim, tol


.. autoclass:: sagemaker.LDAModel
 :members:
 :undoc-members:
 :show-inheritance:

.. autoclass:: sagemaker.LDAPredictor
 :members:
 :undoc-members:
 :show-inheritance:

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Add FM and LDA to the documentation.#66

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 Merged

lukmis
merged 2 commits into

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Feb 6, 2018
 Merged


Add FM and LDA to the documentation.
#66
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e3a8818

Add FM and LDA to the documentation.


lukmis Jan 31, 2018
b5681fa

Merge branch 'master' into docs_fix


yangaws Feb 5, 2018

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6 changes: 3 additions & 3 deletions
README.rst

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -39,7 +39,7 @@ You can install from source by cloning this repository and issuing a pip install

 git clone https://github.com/aws/sagemaker-python-sdk.git
 python setup.py sdist
 pip install dist/sagemaker-1.0.0.tar.gz
 pip install dist/sagemaker-1.0.3.tar.gz

Supported Python versions
~~~~~~~~~~~~~~~~~~~~~~~~~
Expand DownExpand Up@@ -1447,11 +1447,11 @@ Amazon SageMaker provides several built-in machine learning algorithms that you

The full list of algorithms is available on the AWS website: https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html

SageMaker Python SDK includes Estimator wrappers for the AWS K-means, Principal Components Analysis, and Linear Learner algorithms.
SageMaker Python SDK includes Estimator wrappers for the AWS K-means, Principal Components Analysis, Linear Learner, Factorization Machines and LDA algorithms.

Definition and usage
~~~~~~~~~~~~~~~~~~~~
Estimators that wrap Amazon's built-in algorithms define algorithm's hyperparameters with defaults. When a default is not possible you need to provide the value during construction:
Estimators that wrap Amazon's built-in algorithms define algorithm's hyperparameters with defaults. When a default is not possible you need to provide the value during construction, e.g.:

- ``KMeans`` Estimator requires parameter ``k`` to define number of clusters
- ``PCA`` Estimator requires parameter ``num_components`` to define number of principal components
Expand Down

2 changes: 1 addition & 1 deletion
doc/conf.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -18,7 +18,7 @@ def __getattr__(cls, name):
 'tensorflow.python.framework', 'tensorflow_serving', 'tensorflow_serving.apis']
sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES)

version = '1.0'
version = '1.0.3'
project = u'sagemaker'

# Add any Sphinx extension module names here, as strings. They can be extensions
Expand Down

22 changes: 22 additions & 0 deletions
doc/factorization_machines.rst

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,22 @@
FactorizationMachines
-------------------------

The Amazon SageMaker Factorization Machines algorithm.

.. autoclass:: sagemaker.FactorizationMachines
 :members:
 :undoc-members:
 :show-inheritance:
 :inherited-members:
 :exclude-members: image, num_factors, predictor_type, epochs, clip_gradient, mini_batch_size, feature_dim, eps, rescale_grad, bias_lr, linear_lr, factors_lr, bias_wd, linear_wd, factors_wd, bias_init_method, bias_init_scale, bias_init_sigma, bias_init_value, linear_init_method, linear_init_scale, linear_init_sigma, linear_init_value, factors_init_method, factors_init_scale, factors_init_sigma, factors_init_value


.. autoclass:: sagemaker.FactorizationMachinesModel
 :members:
 :undoc-members:
 :show-inheritance:

.. autoclass:: sagemaker.FactorizationMachinesPredictor
 :members:
 :undoc-members:
 :show-inheritance:

5 changes: 4 additions & 1 deletion
doc/index.rst

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -38,11 +38,14 @@ A managed environment for TensorFlow training and hosting on Amazon SageMaker

SageMaker First-Party Algorithms
--------------------------------
Amazon provides implementations of some common machine learning algortithms optimized for GPU archicture and massive datasets.
Amazon provides implementations of some common machine learning algortithms optimized for GPU architecture and massive datasets.

.. toctree::
 :maxdepth: 2

 kmeans
 pca
 linear_learner
 sagemaker.amazon.amazon_estimator
 factorization_machines
 lda

22 changes: 22 additions & 0 deletions
doc/lda.rst

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,22 @@
LDA
--------------------

The Amazon SageMaker LDA algorithm.

.. autoclass:: sagemaker.LDA
 :members:
 :undoc-members:
 :show-inheritance:
 :inherited-members:
 :exclude-members: image, num_topics, alpha0, max_restarts, max_iterations, mini_batch_size, feature_dim, tol


.. autoclass:: sagemaker.LDAModel
 :members:
 :undoc-members:
 :show-inheritance:

.. autoclass:: sagemaker.LDAPredictor
 :members:
 :undoc-members:
 :show-inheritance:

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
Add FM and LDA to the documentation.#66

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Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

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Already on GitHub?
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 Merged

lukmis
merged 2 commits into

from

Feb 6, 2018
 Merged


Add FM and LDA to the documentation.
#66
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e3a8818

Add FM and LDA to the documentation.


lukmis Jan 31, 2018
b5681fa

Merge branch 'master' into docs_fix


yangaws Feb 5, 2018

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6 changes: 3 additions & 3 deletions
README.rst

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -39,7 +39,7 @@ You can install from source by cloning this repository and issuing a pip install

 git clone https://github.com/aws/sagemaker-python-sdk.git
 python setup.py sdist
 pip install dist/sagemaker-1.0.0.tar.gz
 pip install dist/sagemaker-1.0.3.tar.gz

Supported Python versions
~~~~~~~~~~~~~~~~~~~~~~~~~
Expand DownExpand Up@@ -1447,11 +1447,11 @@ Amazon SageMaker provides several built-in machine learning algorithms that you

The full list of algorithms is available on the AWS website: https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html

SageMaker Python SDK includes Estimator wrappers for the AWS K-means, Principal Components Analysis, and Linear Learner algorithms.
SageMaker Python SDK includes Estimator wrappers for the AWS K-means, Principal Components Analysis, Linear Learner, Factorization Machines and LDA algorithms.

Definition and usage
~~~~~~~~~~~~~~~~~~~~
Estimators that wrap Amazon's built-in algorithms define algorithm's hyperparameters with defaults. When a default is not possible you need to provide the value during construction:
Estimators that wrap Amazon's built-in algorithms define algorithm's hyperparameters with defaults. When a default is not possible you need to provide the value during construction, e.g.:

- ``KMeans`` Estimator requires parameter ``k`` to define number of clusters
- ``PCA`` Estimator requires parameter ``num_components`` to define number of principal components
Expand Down

2 changes: 1 addition & 1 deletion
doc/conf.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -18,7 +18,7 @@ def __getattr__(cls, name):
 'tensorflow.python.framework', 'tensorflow_serving', 'tensorflow_serving.apis']
sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES)

version = '1.0'
version = '1.0.3'
project = u'sagemaker'

# Add any Sphinx extension module names here, as strings. They can be extensions
Expand Down

22 changes: 22 additions & 0 deletions
doc/factorization_machines.rst

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,22 @@
FactorizationMachines
-------------------------

The Amazon SageMaker Factorization Machines algorithm.

.. autoclass:: sagemaker.FactorizationMachines
 :members:
 :undoc-members:
 :show-inheritance:
 :inherited-members:
 :exclude-members: image, num_factors, predictor_type, epochs, clip_gradient, mini_batch_size, feature_dim, eps, rescale_grad, bias_lr, linear_lr, factors_lr, bias_wd, linear_wd, factors_wd, bias_init_method, bias_init_scale, bias_init_sigma, bias_init_value, linear_init_method, linear_init_scale, linear_init_sigma, linear_init_value, factors_init_method, factors_init_scale, factors_init_sigma, factors_init_value


.. autoclass:: sagemaker.FactorizationMachinesModel
 :members:
 :undoc-members:
 :show-inheritance:

.. autoclass:: sagemaker.FactorizationMachinesPredictor
 :members:
 :undoc-members:
 :show-inheritance:

5 changes: 4 additions & 1 deletion
doc/index.rst

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -38,11 +38,14 @@ A managed environment for TensorFlow training and hosting on Amazon SageMaker

SageMaker First-Party Algorithms
--------------------------------
Amazon provides implementations of some common machine learning algortithms optimized for GPU archicture and massive datasets.
Amazon provides implementations of some common machine learning algortithms optimized for GPU architecture and massive datasets.

.. toctree::
 :maxdepth: 2

 kmeans
 pca
 linear_learner
 sagemaker.amazon.amazon_estimator
 factorization_machines
 lda

22 changes: 22 additions & 0 deletions
doc/lda.rst

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,22 @@
LDA
--------------------

The Amazon SageMaker LDA algorithm.

.. autoclass:: sagemaker.LDA
 :members:
 :undoc-members:
 :show-inheritance:
 :inherited-members:
 :exclude-members: image, num_topics, alpha0, max_restarts, max_iterations, mini_batch_size, feature_dim, tol


.. autoclass:: sagemaker.LDAModel
 :members:
 :undoc-members:
 :show-inheritance:

.. autoclass:: sagemaker.LDAPredictor
 :members:
 :undoc-members:
 :show-inheritance:

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content
Add FM and LDA to the documentation.#66

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Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

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Already on GitHub?
Sign in
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 Merged

lukmis
merged 2 commits into

from

Feb 6, 2018
 Merged


Add FM and LDA to the documentation.
#66
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e3a8818

Add FM and LDA to the documentation.


lukmis Jan 31, 2018
b5681fa

Merge branch 'master' into docs_fix


yangaws Feb 5, 2018

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6 changes: 3 additions & 3 deletions
README.rst

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -39,7 +39,7 @@ You can install from source by cloning this repository and issuing a pip install

 git clone https://github.com/aws/sagemaker-python-sdk.git
 python setup.py sdist
 pip install dist/sagemaker-1.0.0.tar.gz
 pip install dist/sagemaker-1.0.3.tar.gz

Supported Python versions
~~~~~~~~~~~~~~~~~~~~~~~~~
Expand DownExpand Up@@ -1447,11 +1447,11 @@ Amazon SageMaker provides several built-in machine learning algorithms that you

The full list of algorithms is available on the AWS website: https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html

SageMaker Python SDK includes Estimator wrappers for the AWS K-means, Principal Components Analysis, and Linear Learner algorithms.
SageMaker Python SDK includes Estimator wrappers for the AWS K-means, Principal Components Analysis, Linear Learner, Factorization Machines and LDA algorithms.

Definition and usage
~~~~~~~~~~~~~~~~~~~~
Estimators that wrap Amazon's built-in algorithms define algorithm's hyperparameters with defaults. When a default is not possible you need to provide the value during construction:
Estimators that wrap Amazon's built-in algorithms define algorithm's hyperparameters with defaults. When a default is not possible you need to provide the value during construction, e.g.:

- ``KMeans`` Estimator requires parameter ``k`` to define number of clusters
- ``PCA`` Estimator requires parameter ``num_components`` to define number of principal components
Expand Down

2 changes: 1 addition & 1 deletion
doc/conf.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -18,7 +18,7 @@ def __getattr__(cls, name):
 'tensorflow.python.framework', 'tensorflow_serving', 'tensorflow_serving.apis']
sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES)

version = '1.0'
version = '1.0.3'
project = u'sagemaker'

# Add any Sphinx extension module names here, as strings. They can be extensions
Expand Down

22 changes: 22 additions & 0 deletions
doc/factorization_machines.rst

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,22 @@
FactorizationMachines
-------------------------

The Amazon SageMaker Factorization Machines algorithm.

.. autoclass:: sagemaker.FactorizationMachines
 :members:
 :undoc-members:
 :show-inheritance:
 :inherited-members:
 :exclude-members: image, num_factors, predictor_type, epochs, clip_gradient, mini_batch_size, feature_dim, eps, rescale_grad, bias_lr, linear_lr, factors_lr, bias_wd, linear_wd, factors_wd, bias_init_method, bias_init_scale, bias_init_sigma, bias_init_value, linear_init_method, linear_init_scale, linear_init_sigma, linear_init_value, factors_init_method, factors_init_scale, factors_init_sigma, factors_init_value


.. autoclass:: sagemaker.FactorizationMachinesModel
 :members:
 :undoc-members:
 :show-inheritance:

.. autoclass:: sagemaker.FactorizationMachinesPredictor
 :members:
 :undoc-members:
 :show-inheritance:

5 changes: 4 additions & 1 deletion
doc/index.rst

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -38,11 +38,14 @@ A managed environment for TensorFlow training and hosting on Amazon SageMaker

SageMaker First-Party Algorithms
--------------------------------
Amazon provides implementations of some common machine learning algortithms optimized for GPU archicture and massive datasets.
Amazon provides implementations of some common machine learning algortithms optimized for GPU architecture and massive datasets.

.. toctree::
 :maxdepth: 2

 kmeans
 pca
 linear_learner
 sagemaker.amazon.amazon_estimator
 factorization_machines
 lda

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doc/lda.rst

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LDA
--------------------

The Amazon SageMaker LDA algorithm.

.. autoclass:: sagemaker.LDA
 :members:
 :undoc-members:
 :show-inheritance:
 :inherited-members:
 :exclude-members: image, num_topics, alpha0, max_restarts, max_iterations, mini_batch_size, feature_dim, tol


.. autoclass:: sagemaker.LDAModel
 :members:
 :undoc-members:
 :show-inheritance:

.. autoclass:: sagemaker.LDAPredictor
 :members:
 :undoc-members:
 :show-inheritance: