Here is how the retests for Beijing 2008 and London 2012 work. The IWF announced that today that the executive board unanimously decided that countries with produced three or more doping violations from the 20 retests will be suspended for one year.Countries Affected: Armenia, Azerbaijan, Belarus, China, Moldova, Kazakhstan, Russia, Turkey and Ukraine.(via) Update : Next Wave of 2008 positives (via) So Lydia Valentin and Christine Girard jumped into gold medal spots now.
Some news sites claim they will test all samples, but that doesn’t follow from the source article from the IOC.
This is the critical difference from a regular function.
Note: Since the xrange() is replaced with range in Python 3.x, we should use range() instead for compatibility. That means it does not produce the results in memory any more, and if we want to get list from range(), we need to force it to do so: list(range(...)).
Python defines several iterator objects to support iteration over general and specific sequence types, dictionaries.
That means we can pass them around as objects and can manipulate them.
In other words, most of the times, this just means we can pass these first-class citizens as arguments to functions, or return them from functions. Even things that are "primitive types" in other languages: Everything between the triple quotes (with double quotes, """ or with single quotes,''') is the function's docstring, which documents what the function does.
Here is the code: ) docs.append(text) y.append(label) except Stop Iteration: return None, None return docs, y doc_stream = stream_docs(path='./test.txt') for _ in range(100): X_train, y_train = get_minibatch(doc_stream, Note that the yield makes the stream_docs() to return a generator which is always an iterator: If we comment out the "yield" line, we get "Type Error: None Type object is not an iterator" at the next(doc_stream) in "get_minibatch()" function.
Output from the code: X_train, y_train= ['line-a', 'line-b', 'line-c'] [' 1', ' 2', ' 3'] X_train, y_train= ['line-d', 'line-e', 'line-f'] [' 4', ' 5', ' 6'] X_train, y_train= ['line-g', 'line-h', 'line-i'] [' 7', ' 8', ' 9'] X_train, y_train= ['line-j ', 'line-k ', 'line-k '] ['10', '11', '12'] for x in xrange(101)]) '0123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100' We should be able to answer the questions about the standard library.
Suppose we have a huge data file that has hundred millions of lines. In this case, we may want to take so called out-of-core approach: we process data in batch (partially, one by one) rather than process it at once.
This saves us from the memory issue when we deal with big data set. In the following sample, we do process three lines at a time.
Following allegations by the German documentary of widespread doping the IOC kicked off a reanalysis of Beijing 2008 and London 2012 samples (for all sports).