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minor readme updates
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@ashkonf
ashkonf committed Aug 8, 2020
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Expand Up @@ -24,7 +24,7 @@ Arguments:

| Name | Type | Description | Optional? | Default Value |
|-------------------|---------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------|---------------|
| `transition_weights` | Once nested list or dictionary, or similar. | The graph to which the PageRank algorithm is applied represented as the weights between its nodes. It should be encoded sparsely as a once nested dictionary or a once nested list. If encoded sparsely as a once nested dictionary, keys and nested keys should correspond to node names and values to weights. Other datastructures with the same nested key-value interface, such as certain Pandas matrices, are also acceptable. If encoded as a once nested list, node names are assigned as the indices corresponding to locations in the nested list. | False | N/A |
| `transition_weights` | Once nested list or dictionary, or similar. | The graph to which the PageRank algorithm is applied represented as the weights between its nodes. It should be encoded sparsely as a once nested dictionary or a once nested list. If encoded sparsely as a once nested dictionary, keys and nested keys should correspond to node names and values to weights. Other datastructures with the same nested key-value interface, such as certain Pandas matrices, are also acceptable. If encoded as a once nested list, node names are assigned as the indices corresponding to locations in the nested list. | False | |
| `rsp` | `float` | The graph to which the PageRank algorithm is applied represented as the weights between its nodes. It should be encoded sparsely as a once nested dictionary or a once nested list. If encoded sparsely as a once nested dictionary, keys and nested keys should correspond to node names and values to weights. Other datastructures with the same nested key-value interface, such as certain Pandas matrices, are also acceptable. If encoded as a once nested list, node names are assigned as the indices corresponding to locations in the nested list. | True | `0.15` |
| `epsilon` | `float` | The threshold of convergence. If the Euclidean norm of the difference between the approximations of the steady state vector before and after an iteration of power iteration is smaller than epsilon, the algorithm will consider itself to have converged and will terminate. | True | `0.00001` |
| `max_iterations` | `int` | The number of iterations after which power iteration will be terminated even if it has not yet converged. | True | `1000` |
Expand Down Expand Up @@ -60,7 +60,7 @@ Arguments:

| Name | Type | Description | Optional? | Default Value |
|-----------------|-----------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------|---------------|
| `document` | `str` | A string representing a document. Note that all characters in the string must be standard ASCII characters to avoid exceptions. | False | N/A |
| `document` | `str` | A string representing a document. Note that all characters in the string must be standard ASCII characters to avoid exceptions. | False | |
| `windowSize` | `int` | The width of the window in which two words must fall to be considered to have co-occured. For a window size of 2, the default value of this argument, a word will be considered to have co-occurred with any word one or two words away from it in the document. | True | `2` |
| `rsp` | `float` | Again the random surfer probability that represents the probability with which the random walk through the graph will deviate from its edges and instead jump randomly to any node in the graph. | True | `0.15` |
| `relevant_pos_tags` | `[str]` | The TextRank algorithm will filter the words in a document down to only those of certain parts of speech. The default implementation of the algorithm only considers nouns and adjectives. See the original paper that proposed TextRank for a justification of this decision. | True | `["NN", "ADJ"]` |
Expand All @@ -78,7 +78,7 @@ Arguments:

| Name | Type | Description | Optional? | Default Value |
|----------|---------------------------------------------|---------------------------------------------------------------------------------------------------------|----------|---------------|
| `file_name` | Once nested list or dictionary, or similar. | The name or full path of the file that contains the document the TextRank algorithm will be applied to. | False | N/A |
| `file_name` | Once nested list or dictionary, or similar. | The name or full path of the file that contains the document the TextRank algorithm will be applied to. | False | |
| `title` | `str` | The document's title, an optional argument used only in printed progress indicators. | True | `"a document"` |

Return value: This function has no return value, and instead prints out its results.
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