Python random.shuffle函数代码示例

def loadData():
    #filenames = os.listdir(os.getcwd())
    filenames = [dataFile]
    for filename in filenames:
        if 'txt' in filename and 'sum' not in filename:
            f = open(filename)
    lines = f.readlines()
    f.close()
    random.shuffle(lines)
    data = []
    label = []
    for i in range(len(lines)):
        line = lines[i][:]
        lines[i] = ''
        pos = line.find(' ')
        if pos < 0:
            continue
        line = line[pos+1 :].strip()
        spLine = line.split(' ')
        if int(spLine[-1]) < 100:
            spLine[-1] = 0
        elif int(spLine[-1]) > 100:
            spLine[-1] = 1
        else:
            continue
        data.append(spLine[:-1])
        label.append(spLine[-1])
    print 'array...'
    data = np.array(data, dtype = float)
    label = np.array(label, dtype = int)
    print 'score...'
    weight = getWeight(label)
    return (data, label, weight)

 def SGD(self, training_data, epochs, mini_batch_size, eta,
         test_data=None, weight_decay = 0.0):
     """Train the neural network using mini-batch stochastic
     gradient descent.  The ``training_data`` is a list of tuples
     ``(x, y)`` representing the training inputs and the desired
     outputs.  The other non-optional parameters are
     self-explanatory.  If ``test_data`` is provided then the
     network will be evaluated against the test data after each
     epoch, and partial progress printed out.  This is useful for
     tracking progress, but slows things down substantially."""
     if test_data: n_test = len(test_data)
     n = len(training_data)
     for j in xrange(epochs):
         random.shuffle(training_data)
         mini_batches = [
             training_data[k:k+mini_batch_size]
             for k in xrange(0, n, mini_batch_size)]
         for mini_batch in mini_batches:
             self.update_mini_batch(mini_batch, eta , weight_decay)
         if test_data:
             n_correct = float(self.evaluate(test_data))
             print "Epoch {0}: {1} / {2}".format(
                 j,n_correct , n_test)
             self.test_accuracy.append(float('%.4f'%(n_correct/n_test)))
         else:
             print "Epoch {0} complete".format(j)
         
         self.train_costs.append(self.cost_val(training_data))
         print "Epoch {0}: cost = ".format(j),self.train_costs[-1]

    def _shuffle_slides( self ):
        # randomize the groups and create our play list
        shuffle( self.tmp_slides )
        # now create our final playlist
        print "-----------------------------------------"
        # loop thru slide groups and skip already watched groups
        for slides in self.tmp_slides:
            # has this group been watched
            if ( not self.settings[ "trivia_unwatched_only" ] or ( slides[ 0 ] and xbmc.getCacheThumbName( slides[ 0 ] ) not in self.watched ) or
                  ( slides[ 1 ] and xbmc.getCacheThumbName( slides[ 1 ] ) not in self.watched ) or
                  ( slides[ 2 ] and xbmc.getCacheThumbName( slides[ 2 ] ) not in self.watched ) ):
                # loop thru slide group only include non blank slides
                for slide in slides:
                    # only add if non blank
                    if ( slide ):
                        # add slide
                        self.slide_playlist += [ slide ]

                print "included - %s, %s, %s" % ( os.path.basename( slides[ 0 ] ), os.path.basename( slides[ 1 ] ), os.path.basename( slides[ 2 ] ), )
            else:
                print "----------------------------------------------------"
                print "skipped - %s, %s, %s" % ( os.path.basename( slides[ 0 ] ), os.path.basename( slides[ 1 ] ), os.path.basename( slides[ 2 ] ), )
                print "----------------------------------------------------"
        print
        print "total slides selected: %d" % len( self.slide_playlist )
        print

    def test_sort_index_multicolumn(self):
        import random
        A = np.arange(5).repeat(20)
        B = np.tile(np.arange(5), 20)
        random.shuffle(A)
        random.shuffle(B)
        frame = DataFrame({'A': A, 'B': B,
                           'C': np.random.randn(100)})

        # use .sort_values #9816
        with tm.assert_produces_warning(FutureWarning):
            frame.sort_index(by=['A', 'B'])
        result = frame.sort_values(by=['A', 'B'])
        indexer = np.lexsort((frame['B'], frame['A']))
        expected = frame.take(indexer)
        assert_frame_equal(result, expected)

        # use .sort_values #9816
        with tm.assert_produces_warning(FutureWarning):
            frame.sort_index(by=['A', 'B'], ascending=False)
        result = frame.sort_values(by=['A', 'B'], ascending=False)
        indexer = np.lexsort((frame['B'].rank(ascending=False),
                              frame['A'].rank(ascending=False)))
        expected = frame.take(indexer)
        assert_frame_equal(result, expected)

        # use .sort_values #9816
        with tm.assert_produces_warning(FutureWarning):
            frame.sort_index(by=['B', 'A'])
        result = frame.sort_values(by=['B', 'A'])
        indexer = np.lexsort((frame['A'], frame['B']))
        expected = frame.take(indexer)
        assert_frame_equal(result, expected)

def loadArray(dirpath):
    # pattern = regex = str variable = '.+\.label' (recommended)
    pattern = '.+\.label'
    # another = 'array' (recommended)
    another = 'array'
    names = os.listdir(dirpath)
    random.shuffle(names)
    for name in names:
        if re.match(pattern,name) != None:
            #print name
            folder,prename,num,suffix = name.split('.')
            target = folder + '.' + prename + '.' + num + '.' + another
            targetpath = dirpath + '/' + target
            # find another suffix data file
            # meanwhile examine the num, length of spectrogram = length of label
            if os.path.exists(targetpath):
                # extract object from a file
                with file(target,'rb') as f:
                    spectroArray = cPickle.load(f)
                    # GPU default type is float32
                    spectroArray = np.float32(spectroArray)
                with file(name,'rb') as f:
                    labelArray = cPickle.load(f)
                    # label should be int type
                    labelArray = np.int32(labelArray)
                yield spectroArray,labelArray,int(num)

def shuffle_val(X,y,ratio):
    data = []
    data_size = X.shape[0]
    feature_size = X.shape[1]
    train_data_size = int(data_size * ratio)

    for i in range(data_size):
        tmp_X = X[i]
        one_line = np.concatenate((tmp_X,y[i]))
        data.append(one_line)

    random.shuffle(data)

    split_index = [0,int(data_size*ratio),data_size]

    X = np.zeros((data_size,feature_size))
    y = np.zeros((data_size,1))
    for i in range(data_size):
        X[i] = data[i][:feature_size]
        y[i] = data[i][feature_size]

    X_train = np.array(X[split_index[0]:split_index[1]])
    X_val = np.array(X[split_index[1]:split_index[2]])
    y_train = np.array(y[split_index[0]:split_index[1]])
    y_val = np.array(y[split_index[1]:split_index[2]])

    return X_train,y_train,X_val,y_val

	def shuffle(self):
		'''
		Shuffles the cards
			Args: None
			Returns: None
		'''
		random.shuffle(self.cards)

    def __init__(self, cache_path, *, max_size=None):

        self._cache_path = cache_path
        self.max_size = max_size
        # convert to bytes
        if self.max_size is not None:
            self.max_size *= 1048576

        # TODO 2k compat
        os.makedirs(cache_path, exist_ok=True)
        self._fn_cache = dict()
        self._sz_cache = dict()
        # TODO replace this with a double linked list like boltons LRU
        self._heap_map = dict()
        self._heap = []

        # put files in to heap in random order
        files = glob(os.path.join(self._cache_path, '*feather'))
        shuffle(files)
        for fn in files:
            key = self._key_from_filename(fn)
            self._fn_cache[key] = fn
            stat = os.stat(fn)
            self._sz_cache[key] = stat.st_size
            heap_entry = [time.time(), key]
            heapq.heappush(self._heap, heap_entry)
            self._heap_map[key] = heap_entry

        # prune up front just in case
        self.__prune_files()

def choose_next_neighbour(routes_choices, chosen, city):
    neighbours=list(routes_choices[city]-chosen)
    if len(neighbours):
        random.shuffle(neighbours)
        count, neighbour=min((len(routes_choices[n]), n) for n in neighbours)
        return neighbour
    return None

def fping(ips):

# IP tomb betoltese

    rv = loads(ips)

# IP cimek osszekeverese

    shuffle(rv)

# tomeges pingeleshez hasznalt fping parancs parameterezese

    array = ['fping', '-e']

# tomeges pingeleshez hasznalt ipcimek hozzaadasa a parancshoz

    for x in rv:
        array.append(x)

# tomeges pingeles lefuttatasa csovezetekkel visszaterve

    p1 = subprocess.Popen(array, stdout=subprocess.PIPE)
    (pings, err) = p1.communicate()
    #output={}
    output = []
    pings_arr = pings.split('\n')
    for i in range(len(rv)):
		tdict={}
        pings_line = pings_arr[i].split(' ')
        tdict["ip"]=pings_line[0]
        tdict["avg"]= (pings_line[3])[1:]
        #output[pings_line[0]] = (pings_line[3])[1:]
        output.append(tdict)

 def join(self):
     logger.log("We will try to join our seeds members", self.seeds, part='gossip')
     tmp = self.seeds
     others = []
     if not len(self.seeds):
         logger.log("No seeds nodes, I'm a bootstrap node?")
         return
     
     for e in tmp:
         elts = e.split(':')
         addr = elts[0]
         port = self.port
         if len(elts) > 1:
             port = int(elts[1])
         others.append( (addr, port) )
     random.shuffle(others)
     while True:
         logger.log('JOINING myself %s is joining %s nodes' % (self.name, others), part='gossip')
         nb = 0
         for other in others:
             nb += 1
             r = self.do_push_pull(other)
             
             # Do not merge with more than KGOSSIP distant nodes
             if nb > KGOSSIP:
                 continue
         # If we got enough nodes, we exit
         if len(self.nodes) != 1 or self.interrupted or self.bootstrap:
             return
         # Do not hummer the cpu....
         time.sleep(0.1)

    def get_batches_fn(batch_size):
        """
        Create batches of training data
        :param batch_size: Batch Size
        :return: Batches of training data
        """
        image_paths = glob(os.path.join(data_folder, 'image_2', '*.png'))
        label_paths = {
            re.sub(r'_(lane|road)_', '_', os.path.basename(path)): path
            for path in glob(os.path.join(data_folder, 'gt_image_2', '*_road_*.png'))}
        background_color = np.array([255, 0, 0])

        random.shuffle(image_paths)
        for batch_i in range(0, len(image_paths), batch_size):
            images = []
            gt_images = []
            for image_file in image_paths[batch_i:batch_i+batch_size]:
                gt_image_file = label_paths[os.path.basename(image_file)]

                image = scipy.misc.imresize(scipy.misc.imread(image_file), image_shape)
                gt_image = scipy.misc.imresize(scipy.misc.imread(gt_image_file), image_shape)

                gt_bg = np.all(gt_image == background_color, axis=2)
                gt_bg = gt_bg.reshape(*gt_bg.shape, 1)
                gt_image = np.concatenate((gt_bg, np.invert(gt_bg)), axis=2)

                images.append(image)
                gt_images.append(gt_image)

            yield np.array(images), np.array(gt_images)

def add_bias_to_fitness(rawfitness, bias):
    '''
        Derive new fitness values which incorporate codon bias.
    '''
    new_fitness = np.zeros(61)
    
    for i in range(len(genetic_code)):
        # Determine the new preferred, non-preferred frequencies
        family = genetic_code[i]
        aa_fit = rawfitness[ codons.index(genetic_code[i][0]) ]
        k = len(family) - 1.
          
        nonpref = abs(aa_fit) * bias * -1 # Reduce fitness by 50-100%
        pref = deepcopy(aa_fit)
        
        # Assign randomly
        indices = [codons.index(x) for x in family]
        shuffle(indices)
        first = True
        for ind in indices:
            if first:
                new_fitness[ind] = pref
                first=False
            else:
                new_fitness[ind] = nonpref
    
    return new_fitness

 def random_population(self, k):
     population = []
     for i in xrange(0, k):
         x = range(0, self.instance.solution_size())
         random.shuffle(x)
         population.append(x)
     return population

 def get_context_data(self, **kwargs):
     context = super(Home, self).get_context_data(**kwargs)
     member = self.request.user
     recommended_items = []
     if member.is_authenticated():
         for item in get_all_recommended(member, 12):
             if isinstance(item, Movie):
                 item.type = 'movie'
             else:
                 item.type = 'series'
                 size = 0
                 episodes = SeriesEpisode.objects.filter(series=item)
                 for episode in episodes:
                     size += episode.size
                 item.size = size
             recommended_items.append(item)
         if len(recommended_items) < Movie.MIN_RECOMMENDED:
             additional = Movie.MIN_RECOMMENDED - len(recommended_items)
             additional_items = Movie.objects.all().order_by('-release')[:additional]
             recommended_items.append(additional_items)
     context['items'] = recommended_items
     context['recommended_items'] = as_matrix(recommended_items, 4)
     recent_releases = list(Movie.objects.all().order_by('-release', '-id')[:Movie.MAX_RECENT])
     shuffle(recent_releases)
     sample_media = recent_releases[0]
     context['fb_share_item'] = sample_media
     return context