python类sum()的实例源码

def test_zeros(self):
        types = np.typecodes['AllInteger'] + np.typecodes['AllFloat']
        for dt in types:
            d = np.zeros((13,), dtype=dt)
            assert_equal(np.count_nonzero(d), 0)
            # true for ieee floats
            assert_equal(d.sum(), 0)
            assert_(not d.any())

            d = np.zeros(2, dtype='(2,4)i4')
            assert_equal(np.count_nonzero(d), 0)
            assert_equal(d.sum(), 0)
            assert_(not d.any())

            d = np.zeros(2, dtype='4i4')
            assert_equal(np.count_nonzero(d), 0)
            assert_equal(d.sum(), 0)
            assert_(not d.any())

            d = np.zeros(2, dtype='(2,4)i4, (2,4)i4')
            assert_equal(np.count_nonzero(d), 0)

def test_zeros(self):
        types = np.typecodes['AllInteger'] + np.typecodes['AllFloat']
        for dt in types:
            d = np.zeros((13,), dtype=dt)
            assert_equal(np.count_nonzero(d), 0)
            # true for ieee floats
            assert_equal(d.sum(), 0)
            assert_(not d.any())

            d = np.zeros(2, dtype='(2,4)i4')
            assert_equal(np.count_nonzero(d), 0)
            assert_equal(d.sum(), 0)
            assert_(not d.any())

            d = np.zeros(2, dtype='4i4')
            assert_equal(np.count_nonzero(d), 0)
            assert_equal(d.sum(), 0)
            assert_(not d.any())

            d = np.zeros(2, dtype='(2,4)i4, (2,4)i4')
            assert_equal(np.count_nonzero(d), 0)

def test_zeros(self):
        types = np.typecodes['AllInteger'] + np.typecodes['AllFloat']
        for dt in types:
            d = np.zeros((13,), dtype=dt)
            assert_equal(np.count_nonzero(d), 0)
            # true for ieee floats
            assert_equal(d.sum(), 0)
            assert_(not d.any())

            d = np.zeros(2, dtype='(2,4)i4')
            assert_equal(np.count_nonzero(d), 0)
            assert_equal(d.sum(), 0)
            assert_(not d.any())

            d = np.zeros(2, dtype='4i4')
            assert_equal(np.count_nonzero(d), 0)
            assert_equal(d.sum(), 0)
            assert_(not d.any())

            d = np.zeros(2, dtype='(2,4)i4, (2,4)i4')
            assert_equal(np.count_nonzero(d), 0)

def dot(a1, a2):
 # internally: for matrix-matrix multiplies only; vectors are treated like special cases.
 a1 = as_garray(a1); a2 = as_garray(a2)
 if a1.ndim==0 or a2.ndim==0: return a1*a2
 if a1.ndim==a2.ndim==1:
  if a1 is a2: return sum(a1**2)
  else: return dot(a1.reshape(1, a1.size), a2.reshape(a2.size, 1)).item()
 if a1.ndim==2 and a2.ndim==1: return dot(a1, a2.reshape(a2.size, 1)).ravel() # treat a2 like a column vector
 if a1.ndim==1 and a2.ndim==2: return dot(a1._add_axes(2), a2)[0]   # treat a1 like a row vector
 if a1.shape[-1] != a2.shape[-2]: raise ValueError('arrays not aligned for dot product. a dot product was requested of arrays with shapes %s and %s' % (a1.shape, a2.shape))
 if a1.ndim==a2.ndim==2:
  retShape = (a1.shape[0], a2.shape[1])
  if a1.shape[1]==0: return zeros(retShape) # cudamat bug workaround
  ret = empty(retShape)
  if ret.size!=0: _cudamat.dot(a2._base_as_2d(), a1._base_as_2d(), ret._base_as_2d())
  return ret
 if a1.ndim >= 2 and a2.ndim >= 2:
  # this is not necessarily fast, because if a2.ndim>=3 then it involves a transpose
  a12 = ( a1.reshape_2d(-1) if a1.ndim!=2 else a1)
  a22 = ( a2.transpose((a2.ndim-2,) + tuple(xrange(a2.ndim-2)) + (a2.ndim-1,)).reshape_2d(1)
          if a2.ndim!=2 else
          a2)
  retShape = _deleteT2(a1.shape, -1) + _deleteT2(a2.shape, -2)
  return dot(a12, a22).reshape(retShape)
 raise NotImplementedError('dot with arguments of shapes %s and %s' % (a1.shape, a2.shape))

def concatenate(arrays, axis=0):
 arrays = tuple(map(as_garray, arrays))
 if axis<0: axis += arrays[0].ndim
 if not _isSequence(arrays) or not type(axis) in _numberTypes: raise ValueError('wrong argument types to gnumpy.concatenate: expected <arrays> to be a sequence and <axis> to be a number, but got types %s and %s.' % (type(arrays), type(axis)))
 if axis not in range(arrays[0].ndim): raise ValueError('bad axis number (%d) specified (the first array has %d axes)' % (axis, arrays[0].ndim))
 if not _allTheSame( _deleteT2(a.shape, axis) for a in arrays): raise ValueError('array dimensions must agree except possibly for axis #%d. The given array shapes are: %s' % (axis, tuple( a.shape for a in arrays)))
 finalShape = _modifyT(arrays[0].shape, axis, __builtin__.sum( a.shape[axis] for a in arrays))
 if axis==0:
  ret = empty(finalShape)
  nextI = 0
  for a in arrays:
   _cm_row_slice_read(ret._base_shaped(ret.ndim), nextI, nextI+a.size).assign(a._base_shaped(a.ndim))
   nextI += a.size
  return ret
 else:
  return concatenate(tuple([ a.reshape_2d(axis).T for a in arrays]), 0).T.reshape(finalShape)

def test_zeros(self):
        types = np.typecodes['AllInteger'] + np.typecodes['AllFloat']
        for dt in types:
            d = np.zeros((13,), dtype=dt)
            assert_equal(np.count_nonzero(d), 0)
            # true for ieee floats
            assert_equal(d.sum(), 0)
            assert_(not d.any())

            d = np.zeros(2, dtype='(2,4)i4')
            assert_equal(np.count_nonzero(d), 0)
            assert_equal(d.sum(), 0)
            assert_(not d.any())

            d = np.zeros(2, dtype='4i4')
            assert_equal(np.count_nonzero(d), 0)
            assert_equal(d.sum(), 0)
            assert_(not d.any())

            d = np.zeros(2, dtype='(2,4)i4, (2,4)i4')
            assert_equal(np.count_nonzero(d), 0)

def dot(a1, a2):
 # internally: for matrix-matrix multiplies only; vectors are treated like special cases.
 a1 = as_garray(a1); a2 = as_garray(a2)
 if a1.ndim==0 or a2.ndim==0: return a1*a2
 if a1.ndim==a2.ndim==1:
  if a1 is a2: return sum(a1**2)
  else: return dot(a1.reshape(1, a1.size), a2.reshape(a2.size, 1)).item()
 if a1.ndim==2 and a2.ndim==1: return dot(a1, a2.reshape(a2.size, 1)).ravel() # treat a2 like a column vector
 if a1.ndim==1 and a2.ndim==2: return dot(a1._add_axes(2), a2)[0]   # treat a1 like a row vector
 if a1.shape[-1] != a2.shape[-2]: raise ValueError('arrays not aligned for dot product. a dot product was requested of arrays with shapes %s and %s' % (a1.shape, a2.shape))
 if a1.ndim==a2.ndim==2:
  retShape = (a1.shape[0], a2.shape[1])
  if a1.shape[1]==0: return zeros(retShape) # cudamat bug workaround
  ret = empty(retShape)
  if ret.size!=0: _cudamat.dot(a2._base_as_2d(), a1._base_as_2d(), ret._base_as_2d())
  return ret
 if a1.ndim >= 2 and a2.ndim >= 2:
  # this is not necessarily fast, because if a2.ndim>=3 then it involves a transpose
  a12 = ( a1.reshape_2d(-1) if a1.ndim!=2 else a1)
  a22 = ( a2.transpose((a2.ndim-2,) + tuple(xrange(a2.ndim-2)) + (a2.ndim-1,)).reshape_2d(1)
          if a2.ndim!=2 else
          a2)
  retShape = _deleteT2(a1.shape, -1) + _deleteT2(a2.shape, -2)
  return dot(a12, a22).reshape(retShape)
 raise NotImplementedError('dot with arguments of shapes %s and %s' % (a1.shape, a2.shape))

def concatenate(arrays, axis=0):
 arrays = tuple(map(as_garray, arrays))
 if axis<0: axis += arrays[0].ndim
 if not _isSequence(arrays) or not type(axis) in _numberTypes: raise ValueError('wrong argument types to gnumpy.concatenate: expected <arrays> to be a sequence and <axis> to be a number, but got types %s and %s.' % (type(arrays), type(axis)))
 if axis not in range(arrays[0].ndim): raise ValueError('bad axis number (%d) specified (the first array has %d axes)' % (axis, arrays[0].ndim))
 if not _allTheSame( _deleteT2(a.shape, axis) for a in arrays): raise ValueError('array dimensions must agree except possibly for axis #%d. The given array shapes are: %s' % (axis, tuple( a.shape for a in arrays)))
 finalShape = _modifyT(arrays[0].shape, axis, __builtin__.sum( a.shape[axis] for a in arrays))
 if axis==0:
  ret = empty(finalShape)
  nextI = 0
  for a in arrays:
   _cm_row_slice_read(ret._base_shaped(ret.ndim), nextI, nextI+a.size).assign(a._base_shaped(a.ndim))
   nextI += a.size
  return ret
 else:
  return concatenate(tuple([ a.reshape_2d(axis).T for a in arrays]), 0).T.reshape(finalShape)

def test_zeros(self):
        types = np.typecodes['AllInteger'] + np.typecodes['AllFloat']
        for dt in types:
            d = np.zeros((13,), dtype=dt)
            assert_equal(np.count_nonzero(d), 0)
            # true for ieee floats
            assert_equal(d.sum(), 0)
            assert_(not d.any())

            d = np.zeros(2, dtype='(2,4)i4')
            assert_equal(np.count_nonzero(d), 0)
            assert_equal(d.sum(), 0)
            assert_(not d.any())

            d = np.zeros(2, dtype='4i4')
            assert_equal(np.count_nonzero(d), 0)
            assert_equal(d.sum(), 0)
            assert_(not d.any())

            d = np.zeros(2, dtype='(2,4)i4, (2,4)i4')
            assert_equal(np.count_nonzero(d), 0)

def test_zeros(self):
        types = np.typecodes['AllInteger'] + np.typecodes['AllFloat']
        for dt in types:
            d = np.zeros((13,), dtype=dt)
            assert_equal(np.count_nonzero(d), 0)
            # true for ieee floats
            assert_equal(d.sum(), 0)
            assert_(not d.any())

            d = np.zeros(2, dtype='(2,4)i4')
            assert_equal(np.count_nonzero(d), 0)
            assert_equal(d.sum(), 0)
            assert_(not d.any())

            d = np.zeros(2, dtype='4i4')
            assert_equal(np.count_nonzero(d), 0)
            assert_equal(d.sum(), 0)
            assert_(not d.any())

            d = np.zeros(2, dtype='(2,4)i4, (2,4)i4')
            assert_equal(np.count_nonzero(d), 0)

def DownloadThrottlerThread(self):
        while self.threadRun:
            time.sleep(self.updateTime)

            tot_down_size_KB = float(sum(self.DOWNLOAD_RATE_LIMIT_BUFFER)/1024.0)
            download_speed_limit_KBps = float(Prefs['download_speed_limit'])
            if tot_down_size_KB > 0 and download_speed_limit_KBps > 0:
                tot_down_speed_KBps = round(float(tot_down_size_KB/self.updateTime), 3)
                if Prefs['use_debug']:
                    Log('Download Throttler:---> Timestamp:%s | Total Down Speed: %s KB/s | Speed Limit: %s KB/s' % (time.time(), tot_down_speed_KBps, download_speed_limit_KBps))

                if tot_down_speed_KBps > download_speed_limit_KBps:
                    self.throttle = True
                    self.throttleStateSleepTime = round(tot_down_speed_KBps/download_speed_limit_KBps,3)
                    if Prefs['use_debug']:
                        Log("Download Throttler:---> Sleep for %s sec." % self.throttleStateSleepTime)
                    time.sleep(self.throttleStateSleepTime)

            self.reset()

def test_zeros(self):
        types = np.typecodes['AllInteger'] + np.typecodes['AllFloat']
        for dt in types:
            d = np.zeros((13,), dtype=dt)
            assert_equal(np.count_nonzero(d), 0)
            # true for ieee floats
            assert_equal(d.sum(), 0)
            assert_(not d.any())

            d = np.zeros(2, dtype='(2,4)i4')
            assert_equal(np.count_nonzero(d), 0)
            assert_equal(d.sum(), 0)
            assert_(not d.any())

            d = np.zeros(2, dtype='4i4')
            assert_equal(np.count_nonzero(d), 0)
            assert_equal(d.sum(), 0)
            assert_(not d.any())

            d = np.zeros(2, dtype='(2,4)i4, (2,4)i4')
            assert_equal(np.count_nonzero(d), 0)

def test_sum(self):
        d = np.ones(101, dtype=np.bool)
        assert_equal(d.sum(), d.size)
        assert_equal(d[::2].sum(), d[::2].size)
        assert_equal(d[::-2].sum(), d[::-2].size)

        d = np.frombuffer(b'\xff\xff' * 100, dtype=bool)
        assert_equal(d.sum(), d.size)
        assert_equal(d[::2].sum(), d[::2].size)
        assert_equal(d[::-2].sum(), d[::-2].size)

def check_count_nonzero(self, power, length):
        powers = [2 ** i for i in range(length)]
        for i in range(2**power):
            l = [(i & x) != 0 for x in powers]
            a = np.array(l, dtype=np.bool)
            c = builtins.sum(l)
            self.assertEqual(np.count_nonzero(a), c)
            av = a.view(np.uint8)
            av *= 3
            self.assertEqual(np.count_nonzero(a), c)
            av *= 4
            self.assertEqual(np.count_nonzero(a), c)
            av[av != 0] = 0xFF
            self.assertEqual(np.count_nonzero(a), c)

def test_count_nonzero_unaligned(self):
        # prevent mistakes as e.g. gh-4060
        for o in range(7):
            a = np.zeros((18,), dtype=np.bool)[o+1:]
            a[:o] = True
            self.assertEqual(np.count_nonzero(a), builtins.sum(a.tolist()))
            a = np.ones((18,), dtype=np.bool)[o+1:]
            a[:o] = False
            self.assertEqual(np.count_nonzero(a), builtins.sum(a.tolist()))

def test_mean_values(self):
        for mat in [self.rmat, self.cmat, self.omat]:
            for axis in [0, 1]:
                tgt = mat.sum(axis=axis)
                res = _mean(mat, axis=axis) * mat.shape[axis]
                assert_almost_equal(res, tgt)
            for axis in [None]:
                tgt = mat.sum(axis=axis)
                res = _mean(mat, axis=axis) * np.prod(mat.shape)
                assert_almost_equal(res, tgt)

def test_sum(self):
        d = np.ones(101, dtype=np.bool)
        assert_equal(d.sum(), d.size)
        assert_equal(d[::2].sum(), d[::2].size)
        assert_equal(d[::-2].sum(), d[::-2].size)

        d = np.frombuffer(b'\xff\xff' * 100, dtype=bool)
        assert_equal(d.sum(), d.size)
        assert_equal(d[::2].sum(), d[::2].size)
        assert_equal(d[::-2].sum(), d[::-2].size)

def check_count_nonzero(self, power, length):
        powers = [2 ** i for i in range(length)]
        for i in range(2**power):
            l = [(i & x) != 0 for x in powers]
            a = np.array(l, dtype=np.bool)
            c = builtins.sum(l)
            self.assertEqual(np.count_nonzero(a), c)
            av = a.view(np.uint8)
            av *= 3
            self.assertEqual(np.count_nonzero(a), c)
            av *= 4
            self.assertEqual(np.count_nonzero(a), c)
            av[av != 0] = 0xFF
            self.assertEqual(np.count_nonzero(a), c)

def test_count_nonzero_unaligned(self):
        # prevent mistakes as e.g. gh-4060
        for o in range(7):
            a = np.zeros((18,), dtype=np.bool)[o+1:]
            a[:o] = True
            self.assertEqual(np.count_nonzero(a), builtins.sum(a.tolist()))
            a = np.ones((18,), dtype=np.bool)[o+1:]
            a[:o] = False
            self.assertEqual(np.count_nonzero(a), builtins.sum(a.tolist()))

def test_mean_values(self):
        for mat in [self.rmat, self.cmat, self.omat]:
            for axis in [0, 1]:
                tgt = mat.sum(axis=axis)
                res = _mean(mat, axis=axis) * mat.shape[axis]
                assert_almost_equal(res, tgt)
            for axis in [None]:
                tgt = mat.sum(axis=axis)
                res = _mean(mat, axis=axis) * np.prod(mat.shape)
                assert_almost_equal(res, tgt)

def test_sum(self):
        d = np.ones(101, dtype=np.bool)
        assert_equal(d.sum(), d.size)
        assert_equal(d[::2].sum(), d[::2].size)
        assert_equal(d[::-2].sum(), d[::-2].size)

        d = np.frombuffer(b'\xff\xff' * 100, dtype=bool)
        assert_equal(d.sum(), d.size)
        assert_equal(d[::2].sum(), d[::2].size)
        assert_equal(d[::-2].sum(), d[::-2].size)

def check_count_nonzero(self, power, length):
        powers = [2 ** i for i in range(length)]
        for i in range(2**power):
            l = [(i & x) != 0 for x in powers]
            a = np.array(l, dtype=np.bool)
            c = builtins.sum(l)
            self.assertEqual(np.count_nonzero(a), c)
            av = a.view(np.uint8)
            av *= 3
            self.assertEqual(np.count_nonzero(a), c)
            av *= 4
            self.assertEqual(np.count_nonzero(a), c)
            av[av != 0] = 0xFF
            self.assertEqual(np.count_nonzero(a), c)

def test_count_nonzero_unaligned(self):
        # prevent mistakes as e.g. gh-4060
        for o in range(7):
            a = np.zeros((18,), dtype=np.bool)[o+1:]
            a[:o] = True
            self.assertEqual(np.count_nonzero(a), builtins.sum(a.tolist()))
            a = np.ones((18,), dtype=np.bool)[o+1:]
            a[:o] = False
            self.assertEqual(np.count_nonzero(a), builtins.sum(a.tolist()))

def test_mean_values(self):
        for mat in [self.rmat, self.cmat, self.omat]:
            for axis in [0, 1]:
                tgt = mat.sum(axis=axis)
                res = _mean(mat, axis=axis) * mat.shape[axis]
                assert_almost_equal(res, tgt)
            for axis in [None]:
                tgt = mat.sum(axis=axis)
                res = _mean(mat, axis=axis) * np.prod(mat.shape)
                assert_almost_equal(res, tgt)

def status():
 if not usingGpu(): print 'gnumpy is running on the CPU, i.e. in simulation mode. The data type is float%s.' % _precision
 if usingGpu():
  if _boardId==None: print 'gnumpy is planning to run on a GPU, but hasn\'t yet chosen & initialized a board.'
  else: print 'gnumpy is running on GPU board #%d.' % _boardId
 print '%s of gpu memory are in use, of which at least %s can be freed immediately by gnumpy.free_reuse_cache().' % (_n_bytes_str(__memoryInUse), _n_bytes_str(__builtin__.sum( size*len(cms)*4 for size, cms in _cmsForReuse.items())))

def _rand__base(shapeInfo, distribution, zero_d_means_scalar):
 if len(shapeInfo)==1 and _isSequence(shapeInfo[0]): zero_d_means_scalar = False; shapeInfo = shapeInfo[0]
 ret = empty(shapeInfo)
 {'uniform': _cmType.fill_with_rand, 'normal': _cmType.fill_with_randn}[distribution](ret._base)
 if ret.size!=0 and _doExpensiveCheck(): assert ret.sum() < 100 + 2*ret.size, 'numerical gpu error: rand() gave a result>100'
 if len(shapeInfo) == 0 and zero_d_means_scalar: return ret.item()
 else: return ret

def sum(x, axis=None):
 """ On numpy arrays this returns a numpy array; on garrays and other array-likes this returns a garray. """
 return _reductor__base(x, axis, garray.sum, numpy.sum)

def all_real(self):
  """ returns True iff all array elements are regular floats, as opposed to inf's, -inf's, and NaN's.  """
  return (self*0).sum()==0

def sum(self, axis=None): return self._reduction__base('sum', axis)

def mean(self, axis=None): return self.sum(axis) / ( self.size if axis==None else self.shape[axis])