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A shift-reduce parser is a class of efficient, table-driven bottom-up parsing methods for computer languages and other notations formally defined by a grammar. The parsing methods most commonly used for parsing programming languages, LR parsing and its variations, are shift-reduce methods. The precedence parsers used before the invention of LR parsing are also shift-reduce methods. All shift-reduce parsers have similar outward effects, in the incremental order in which they build a parse tree or call specific output actions. A shift-reduce parser scans and parses the input text in one forward pass over the text, without backing up. The parser builds up the parse tree incrementally, bottom up, and left to right, without guessing or backtracking. At every point in this pass, the parser has accumulated a list of subtrees or phrases of the input text that have been already parsed. Those subtrees are not yet joined together because the parser has not yet reached the right end of the syntax pattern that will combine them. Consider the string A = B + C * 2. At step 7 in the example, only "A = B +" has been parsed. Only the shaded lower-left corner of the parse tree exists. None of the parse tree nodes numbered 8 and above exist yet. Nodes 1, 2, 6, and 7 are the roots of isolated subtrees covering all the items 1..7. Node 1 is variable A, node 2 is the delimiter =, node 6 is the summand B, and node 7 is the operator +. These four root nodes are temporarily held in a parse stack. The remaining unparsed portion of the input stream is "C * 2". A shift-reduce parser works by doing some combination of Shift steps and Reduce steps, hence the name. A Shift step advances in the input stream by one symbol. That shifted symbol becomes a new single-node parse tree. A Reduce step applies a completed grammar rule to some of the recent parse trees, joining them together as one tree with a new root symbol. The parser continues with these steps until all of the input has been consumed and all of the parse trees have been reduced to a single tree representing an entire legal input.
James Henderson, Alireza Mohammadshahi
[2]Paolo Prandoni, [3]LCAV, EPFL
This notebook shows a simple way to process audio files (in WAV format)
in a block-by-block fashion; this is useful in two cases
1. when you want to process a data stream in real time but you can
accept a small processing delay; in this case a buffered approach
can yield computational gains
2. when you need to process a large file that does not fit entirely
into