我们从Python开源项目中，提取了以下18个代码示例，用于说明如何使用random.lognormvariate()。
def sample(self): return random.lognormvariate(self.mean, self.std)
def test_heap(self): iterations = 5000 maxblocks = 50 blocks = [] # create and destroy lots of blocks of different sizes for i in range(iterations): size = int(random.lognormvariate(0, 1) * 1000) b = multiprocessing.heap.BufferWrapper(size) blocks.append(b) if len(blocks) > maxblocks: i = random.randrange(maxblocks) del blocks[i] # get the heap object heap = multiprocessing.heap.BufferWrapper._heap # verify the state of the heap all = [] occupied = 0 heap._lock.acquire() self.addCleanup(heap._lock.release) for L in list(heap._len_to_seq.values()): for arena, start, stop in L: all.append((heap._arenas.index(arena), start, stop, stop-start, 'free')) for arena, start, stop in heap._allocated_blocks: all.append((heap._arenas.index(arena), start, stop, stop-start, 'occupied')) occupied += (stop-start) all.sort() for i in range(len(all)-1): (arena, start, stop) = all[i][:3] (narena, nstart, nstop) = all[i+1][:3] self.assertTrue((arena != narena and nstart == 0) or (stop == nstart))
def test_heap(self): iterations = 5000 maxblocks = 50 blocks = [] # create and destroy lots of blocks of different sizes for i in xrange(iterations): size = int(random.lognormvariate(0, 1) * 1000) b = multiprocessing.heap.BufferWrapper(size) blocks.append(b) if len(blocks) > maxblocks: i = random.randrange(maxblocks) del blocks[i] # get the heap object heap = multiprocessing.heap.BufferWrapper._heap # verify the state of the heap all = [] occupied = 0 heap._lock.acquire() self.addCleanup(heap._lock.release) for L in heap._len_to_seq.values(): for arena, start, stop in L: all.append((heap._arenas.index(arena), start, stop, stop-start, 'free')) for arena, start, stop in heap._allocated_blocks: all.append((heap._arenas.index(arena), start, stop, stop-start, 'occupied')) occupied += (stop-start) all.sort() for i in range(len(all)-1): (arena, start, stop) = all[i][:3] (narena, nstart, nstop) = all[i+1][:3] self.assertTrue((arena != narena and nstart == 0) or (stop == nstart))
def interkey_interval(): """in milliseconds""" # return 0 # makes testing faster return (random.lognormvariate(0.0, 0.5) * 30.0) / 1000.0 return float(random.randrange(10, 50)) / 1000.0
def CreatData(): """ 1.????????????? 2.????-2.5?+2.5? 3.?????func_(x) """ RandNumberX = arange(-2.5,2.5,5.0/DALL) RandNumberY=[] X=[] Xc=[] Y = [] Yc=[] for i in range(len(RandNumberX)): if (i+1)%3==0: Xc.append(RandNumberX[i]) else: X.append(RandNumberX[i]) for x in RandNumberX: #RandNumberY.append(func_(x)+random.lognormvariate(0, 1)) #???? RandNumberY.append(func_(x)+ uniform(-0.2, 0.2)) for i in range(len(RandNumberY)): if (i+1)%3==0: Yc.append(RandNumberY[i]) else: Y.append(RandNumberY[i]) return X,Y,Xc,Yc,RandNumberX,RandNumberY
def CreatData(): """ 1.????????????? 2.????-2.5?+2.5? 3.?????func_(x) """ RandNumberX = arange(-2.5,2.5,5.0/DataMaxSize) RandNumberY=[] for x in RandNumberX: #RandNumberY.append(func_(x)+random.lognormvariate(0, 1)) #???? RandNumberY.append(func_(x)+ uniform(-0.2, 0.2)) return RandNumberX,RandNumberY
def _execute(self, sources, alignment_stream, interval): if alignment_stream is None: raise ToolExecutionError("Alignment stream expected") for ti, _ in alignment_stream.window(interval, force_calculation=True): yield StreamInstance(ti, random.lognormvariate(mu=self.mu, sigma=self.sigma))
def test_heap(self): iterations = 5000 maxblocks = 50 blocks = [] # create and destroy lots of blocks of different sizes for i in xrange(iterations): size = int(random.lognormvariate(0, 1) * 1000) b = multiprocessing.heap.BufferWrapper(size) blocks.append(b) if len(blocks) > maxblocks: i = random.randrange(maxblocks) del blocks[i] # XXX There should be a better way to release resources for a # single block if i % maxblocks == 0: import gc; gc.collect() # get the heap object heap = multiprocessing.heap.BufferWrapper._heap # verify the state of the heap all = [] occupied = 0 heap._lock.acquire() self.addCleanup(heap._lock.release) for L in heap._len_to_seq.values(): for arena, start, stop in L: all.append((heap._arenas.index(arena), start, stop, stop-start, 'free')) for arena, start, stop in heap._allocated_blocks: all.append((heap._arenas.index(arena), start, stop, stop-start, 'occupied')) occupied += (stop-start) all.sort() for i in range(len(all)-1): (arena, start, stop) = all[i][:3] (narena, nstart, nstop) = all[i+1][:3] self.assertTrue((arena != narena and nstart == 0) or (stop == nstart))
def crossover(self, ind): """Used by the evolution process to generate a new individual. Notes ----- This is a tweaked version of the classical DE crossover algorithm, the main difference that candidate parameters are generated using a lognormal distribution. Bound handling is achieved by resampling where the candidate solution exceeds +/-1 Parameters ---------- Returns ------- y: deap individual An individual representing a candidate solution, to be assigned a fitness. """ if self._params['neighbours']: a, b, c = random.sample([self.population[i] for i in ind.neighbours], 3) else: a, b, c = random.sample(self.population, 3) y = self.toolbox.clone(a) y.ident = ind.ident y.neighbours = ind.neighbours del y.fitness.values # y should now be a copy of ind with the vector elements from a ident = random.randrange(len(self._params['value_means'])) for i, value in enumerate(y): if i == ident or random.random() < self._params['cxpb']: entry = a[i] + random.lognormvariate(-1.2, 0.5) * \ self._params['diff_weight'] * (b[i] - c[i]) tries = 0 while abs(entry) > 1.0: tries += 1 entry = a[i] + random.lognormvariate(-1.2, 0.5) * \ self._params['diff_weight'] * (b[i] - c[i]) if tries > 10000: entry = a[i] y[i] = entry return y
def _crossover(self, ind): """Used by the evolution process to generate a new individual. Notes ----- This is a tweaked version of the classical DE crossover algorithm, the main difference that candidate parameters are generated using a lognormal distribution. Bound handling is achieved by resampling where the candidate solution exceeds +/-1 Parameters ---------- ind : deap individual Returns ------- y : deap individual An individual representing a candidate solution, to be assigned a fitness. """ if self.neighbours: a, b, c = random.sample([self.population[i] for i in ind.neighbours], 3) else: a, b, c = random.sample(self.population, 3) y = self.toolbox.clone(a) y.ident = ind.ident y.neighbours = ind.neighbours del y.fitness.values # y should now be a copy of ind with the vector elements from a ident = random.randrange(len(self.value_means)) for i, value in enumerate(y): if i == ident or random.random() < self.cxpb: entry = a[i] + random.lognormvariate(-1.2, 0.5) * \ self.diff_weight * (b[i] - c[i]) tries = 0 while abs(entry) > 1.0: tries += 1 entry = a[i] + random.lognormvariate(-1.2, 0.5) * \ self.diff_weight * (b[i] - c[i]) if tries > 10000: entry = a[i] y[i] = entry return y
