Python math.pow() Examples

def calculate_similarity(text1,text2):
    raw1 = jieba.cut(text1)
    raw2 = jieba.cut(text2)
    raw1 = Counter(raw1)
    raw2 = Counter(raw2)
    same_words = set(raw1) & set(raw2)
    if (math.sqrt(len(raw1)) * math.sqrt(len(raw2))) != 0:
        dot_product = 0
        mod1 = 0
        mod2 = 0
        for word in same_words:
            dot_product += raw1[word] * raw2[word]
        for word in raw1:
            mod1 += math.pow(raw1[word],2)
        for word in raw2:
            mod2 += math.pow(raw2[word],2)
        cos = dot_product/math.sqrt(mod1*mod2)
    else:
        cos = 0
    return cos 

def swirl(x, y, step):
    x -= (u_width / 2)
    y -= (u_height / 2)
    dist = math.sqrt(pow(x, 2) + pow(y, 2)) / 2.0
    angle = (step / 10.0) + (dist * 1.5)
    s = math.sin(angle)
    c = math.cos(angle)
    xs = x * c - y * s
    ys = x * s + y * c
    r = abs(xs + ys)
    r = r * 12.0
    r -= 20
    return (r, r + (s * 130), r + (c * 130))


# roto-zooming checker board 

def setValue(self, settings, e):
        if e.index == 1:
            self.logicalName = _GXCommon.toLogicalName(e.value)
        elif e.index == 2:
            if self.scaler != 1 and e.value is not None:
                try:
                    if settings.isServer:
                        self.value = e.value
                    else:
                        self.value = e.value * self.scaler
                except Exception:
                    #  Sometimes scaler is set for wrong Object type.
                    self.value = e.value
            else:
                self.value = e.value
        elif e.index == 3:
            #  Set default values.
            if not e.value:
                self.scaler = 1
                self.unit = Unit.NONE
            else:
                self.scaler = math.pow(10, e.value[0])
                self.unit = Unit(e.value[1])
        else:
            e.error = ErrorCode.READ_WRITE_DENIED 

def _compute_dE(self, pos=None, lengths=None, weights=None, m=None):
        dEx = 0
        dEy = 0
        d2Ex2 = 0
        d2Ey2 = 0
        d2Exy = 0
        d2Eyx = 0
        for i in pos:
            if i != m:
                xmi = pos[m][0] - pos[i][0]
                ymi = pos[m][1] - pos[i][1]
                xmi2 = xmi * xmi
                ymi2 = ymi * ymi
                xmi_ymi2 = xmi2 + ymi2
                lmi = lengths[m][i]
                kmi = weights[m][i] / (lmi * lmi)
                dEx += kmi * (xmi - (lmi * xmi) / math.sqrt(xmi_ymi2))
                dEy += kmi * (ymi - (lmi * ymi) / math.sqrt(xmi_ymi2))
                d2Ex2 += kmi * (1 - (lmi * ymi2) / math.pow(xmi_ymi2, 1.5))
                d2Ey2 += kmi * (1 - (lmi * xmi2) / math.pow(xmi_ymi2, 1.5))
                res = kmi * (lmi * xmi * ymi) / math.pow(xmi_ymi2, 1.5)
                d2Exy += res
                d2Eyx += res
        return dEx, dEy, d2Ex2, d2Ey2, d2Exy, d2Eyx 

def genCubeVector(x, y, z, x_mult=1, y_mult=1, z_mult=1):
    """Generates a map of vector lengths from the center point to each coordinate

    x - width of matrix to generate
    y - height of matrix to generate
    z - depth of matrix to generate
    x_mult - value to scale x-axis by
    y_mult - value to scale y-axis by
    z_mult - value to scale z-axis by
    """
    cX = (x - 1) / 2.0
    cY = (y - 1) / 2.0
    cZ = (z - 1) / 2.0

    def vect(_x, _y, _z):
        return int(math.sqrt(math.pow(_x - cX, 2 * x_mult) +
                             math.pow(_y - cY, 2 * y_mult) +
                             math.pow(_z - cZ, 2 * z_mult)))

    return [[[vect(_x, _y, _z) for _z in range(z)] for _y in range(y)] for _x in range(x)] 

def _free_space(self, directory, power=0):
		"""
			Get available free space at a target directory.

			@param directory: directory path of a folder
			@type directory: basestring

			@return: Available free space
			@rtype: float
		"""
		assert power >= 0
		assert isinstance(directory, basestring)
		assert self.validate_dir(directory)
		if not directory or not os.path.isdir(directory):
			return 0
		statvfs = os.statvfs(directory)
		free_space = statvfs.f_frsize * statvfs.f_bfree
		return free_space / math.pow(1024, power) 

def _load_data(name):
    buf = open(name).read()
    tks = buf.split(' ')
    vocab = {}
    freq = [0]
    data = []
    for tk in tks:
        if len(tk) == 0:
            continue
        if tk not in vocab:
            vocab[tk] = len(vocab) + 1
            freq.append(0)
        wid = vocab[tk]
        data.append(wid)
        freq[wid] += 1
    negative = []
    for i, v in enumerate(freq):
        if i == 0 or v < 5:
            continue
        v = int(math.pow(v * 1.0, 0.75))
        negative += [i for _ in range(v)]
    return data, negative, vocab, freq 

def __call__(self, iteration):
        """
        Call to schedule current learning rate.

        Parameters
        ----------
        iteration: int
            Current iteration count.
        """

        if not self.init:
            self.init = True
            self.old_lr = self.base_lr
        lr = self.base_lr * math.pow(self.factor, int(iteration / self.step))
        if lr != self.old_lr:
            self.old_lr = lr
            logging.info("At Iteration [%d]: Swith to new learning rate %.5f",
                         iteration, lr)
        return lr 

def _gaussian(
        size=3, sigma=0.25, amplitude=1, normalize=False, width=None,
        height=None, sigma_horz=None, sigma_vert=None, mean_horz=0.5,
        mean_vert=0.5):
    # handle some defaults
    if width is None:
        width = size
    if height is None:
        height = size
    if sigma_horz is None:
        sigma_horz = sigma
    if sigma_vert is None:
        sigma_vert = sigma
    center_x = mean_horz * width + 0.5
    center_y = mean_vert * height + 0.5
    gauss = np.empty((height, width), dtype=np.float32)
    # generate kernel
    for i in range(height):
        for j in range(width):
            gauss[i][j] = amplitude * math.exp(-(math.pow((j + 1 - center_x) / (
                sigma_horz * width), 2) / 2.0 + math.pow((i + 1 - center_y) / (sigma_vert * height), 2) / 2.0))
    if normalize:
        gauss = gauss / np.sum(gauss)
    return gauss 

def rampweight(iteration):
    ramp_up_end = 32000
    ramp_down_start = 100000

    if(iteration<ramp_up_end):
        ramp_weight = math.exp(-5 * math.pow((1 - iteration / ramp_up_end),2))
    elif(iteration>ramp_down_start):
        ramp_weight = math.exp(-12.5 * math.pow((1 - (120000 - iteration) / 20000),2)) 
    else:
        ramp_weight = 1 


    if(iteration==0):
        ramp_weight = 0

    return ramp_weight 

def pow(requestContext, seriesList, factor):
    """
    Takes one metric or a wildcard seriesList followed by a constant, and raises the datapoint
    by the power of the constant provided at each point.

    Example:

    .. code-block:: none

      &target=pow(Server.instance01.threads.busy,10)
      &target=pow(Server.instance*.threads.busy,10)

    """
    yield defer.succeed(None)
    for series in seriesList:
        series.name = "pow(%s,%g)" % (series.name, float(factor))
        series.pathExpression = series.name
        for i, value in enumerate(series):
            series[i] = safePow(value, factor)
    returnValue(seriesList) 

def nChooseK(n, k):
    # is n an integer?
    nInt = (math.floor(n) == n)
    if n == k or k == 0:
        return 1
    if (n < k) or (k < 0):
        raise Exception
    if (nInt) and (n < 0.0):
        b = pow(-1.0, k) * math.exp(math.lgamma(abs(n + k)) \
                                  - math.lgamma(k + 1.0)    \
                                  - math.lgamma(abs(n)))
        return round(b)
    if (n >= k):
        b = math.exp(math.lgamma(n + 1.0) - math.lgamma(k + 1.0) \
                   - math.lgamma(n - k + 1.0))
        return round(b)
    if not (nInt) and (n < k):
        b = (1.0/math.pi) * math.exp(math.lgamma(n + 1.0) \
                                   - math.lgamma(k + 1)   \
                                   + math.lgamma(k - n)   \
                   + math.log(math.sin(math.pi * (n - k + 1.0))))
        return round(b)
    return 0.0 

def _gaussian(
        size=3, sigma=0.25, amplitude=1, normalize=False, width=None,
        height=None, sigma_horz=None, sigma_vert=None, mean_horz=0.5,
        mean_vert=0.5):
    # handle some defaults
    if width is None:
        width = size
    if height is None:
        height = size
    if sigma_horz is None:
        sigma_horz = sigma
    if sigma_vert is None:
        sigma_vert = sigma
    center_x = mean_horz * width + 0.5
    center_y = mean_vert * height + 0.5
    gauss = np.empty((height, width), dtype=np.float32)
    # generate kernel
    for i in range(height):
        for j in range(width):
            gauss[i][j] = amplitude * math.exp(-(math.pow((j + 1 - center_x) / (
                sigma_horz * width), 2) / 2.0 + math.pow((i + 1 - center_y) / (sigma_vert * height), 2) / 2.0))
    if normalize:
        gauss = gauss / np.sum(gauss)
    return gauss 

def push(self, time, tput, lat):
        global throughput
        global latency

        for i in range(len(self.half_life)):
            alpha = math.pow(0.5, time / self.half_life[i])
            self.throughput[i] = alpha * self.throughput[i] + (1 - alpha) * tput
            alpha = math.pow(0.5, 1 / self.latency_half_life[i])
            self.latency[i] = alpha * self.latency[i] + (1 - alpha) * lat

        self.weight_throughput += time
        self.weight_latency += 1

        tput = None
        lat = None
        for i in range(len(self.half_life)):
            zero_factor = 1 - math.pow(0.5, self.weight_throughput / self.half_life[i])
            t = self.throughput[i] / zero_factor
            tput = t if tput == None else min(tput, t)  # conservative case is min
            zero_factor = 1 - math.pow(0.5, self.weight_latency / self.latency_half_life[i])
            l = self.latency[i] / zero_factor
            lat = l if lat == None else max(lat, l) # conservative case is max
        throughput = tput
        latency = lat 

def push(self, time, tput, lat):
        global throughput
        global latency

        for i in range(len(self.half_life)):
            alpha = math.pow(0.5, time / self.half_life[i])
            self.throughput[i] = alpha * self.throughput[i] + (1 - alpha) * tput
            alpha = math.pow(0.5, 1 / self.latency_half_life[i])
            self.latency[i] = alpha * self.latency[i] + (1 - alpha) * lat

        self.weight_throughput += time
        self.weight_latency += 1

        tput = None
        lat = None
        for i in range(len(self.half_life)):
            zero_factor = 1 - math.pow(0.5, self.weight_throughput / self.half_life[i])
            t = self.throughput[i] / zero_factor
            tput = t if tput == None else min(tput, t)  # conservative case is min
            zero_factor = 1 - math.pow(0.5, self.weight_latency / self.latency_half_life[i])
            l = self.latency[i] / zero_factor
            lat = l if lat == None else max(lat, l) # conservative case is max
        throughput = tput
        latency = lat 

def push(self, time, tput, lat):
        global throughput
        global latency

        for i in range(len(self.half_life)):
            alpha = math.pow(0.5, time / self.half_life[i])
            self.throughput[i] = alpha * self.throughput[i] + (1 - alpha) * tput
            alpha = math.pow(0.5, 1 / self.latency_half_life[i])
            self.latency[i] = alpha * self.latency[i] + (1 - alpha) * lat

        self.weight_throughput += time
        self.weight_latency += 1

        tput = None
        lat = None
        for i in range(len(self.half_life)):
            zero_factor = 1 - math.pow(0.5, self.weight_throughput / self.half_life[i])
            t = self.throughput[i] / zero_factor
            tput = t if tput == None else min(tput, t)  # conservative case is min
            zero_factor = 1 - math.pow(0.5, self.weight_latency / self.latency_half_life[i])
            l = self.latency[i] / zero_factor
            lat = l if lat == None else max(lat, l) # conservative case is max
        throughput = tput
        latency = lat 

def human_readable_size(size_bytes):
    """Gets a number of bytes as a human readable string.

    Args:
        size_bytes (:obj:`int`): The number of bytes to get as human readable.

    Returns:
        :obj:`str`: The number of bytes in a human readable form.
    """
    if size_bytes == 0:
        return "0B"
    size_name = ("B", "KB", "MB", "GB", "TB", "PB", "EB", "ZB", "YB")
    base = int(math.floor(math.log(size_bytes, 1024)))
    power = math.pow(1024, base)
    size = round(size_bytes / power, 2)
    return "%s %s" % (size, size_name[base]) 

def genVector(width, height, x_mult=1, y_mult=1):
    """
    Generates a map of vector lengths from the center point to each coordinate.

    width - width of matrix to generate
    height - height of matrix to generate
    x_mult - value to scale x-axis by
    y_mult - value to scale y-axis by
    """
    center_x = (width - 1) / 2
    center_y = (height - 1) / 2

    def length(x, y):
        dx = math.pow(x - center_x, 2 * x_mult)
        dy = math.pow(y - center_y, 2 * y_mult)
        return int(math.sqrt(dx + dy))

    return [[length(x, y) for x in range(width)] for y in range(height)] 

def tunnel(x, y, step):
    speed = step / 100.0
    x -= (u_width / 2)
    y -= (u_height / 2)
    xo = math.sin(step / 27.0) * 2
    yo = math.cos(step / 18.0) * 2
    x += xo
    y += yo
    if y == 0:
        if x < 0:
            angle = -(math.pi / 2)
        else:
            angle = (math.pi / 2)
    else:
        angle = math.atan(x / y)
    if y > 0:
        angle += math.pi
    angle /= 2 * math.pi  # convert angle to 0...1 range
    hyp = math.sqrt(math.pow(x, 2) + math.pow(y, 2))
    shade = hyp / 2.1
    shade = 1 if shade > 1 else shade
    angle += speed
    depth = speed + (hyp / 10)
    col1 = hue_to_rgb[step % 255]
    col1 = (col1[0] * 0.8, col1[1] * 0.8, col1[2] * 0.8)
    col2 = hue_to_rgb[step % 255]
    col2 = (col2[0] * 0.3, col2[1] * 0.3, col2[2] * 0.3)
    col = col1 if int(abs(angle * 6.0)) % 2 == 0 else col2
    td = .3 if int(abs(depth * 3.0)) % 2 == 0 else 0
    col = (col[0] + td, col[1] + td, col[2] + td)
    col = (col[0] * shade, col[1] * shade, col[2] * shade)
    return (col[0] * 255, col[1] * 255, col[2] * 255) 

def setValue(self, settings, e):
        #pylint: disable=broad-except
        if e.index == 1:
            self.logicalName = _GXCommon.toLogicalName(e.value)
        elif e.index == 2:
            if self.scaler != 1 and e.value:
                try:
                    if settings.isServer:
                        self.value = e.value
                    else:
                        self.value = e.value * self.scaler
                except Exception:
                    #  Sometimes scaler is set for wrong Object type.
                    self.value = e.value
            else:
                self.value = e.value
        elif e.index == 3:
            #  Set default values.
            if not e.value:
                self.scaler = 0
                self.unit = Unit.NONE
            else:
                if not e.value:
                    self.scaler = 0
                    self.unit = Unit.NONE
                else:
                    self.scaler = math.pow(10, e.value[0])
                    self.unit = Unit(e.value[1])
        elif e.index == 4:
            self.status = e.value
        elif e.index == 5:
            if e.value is None:
                self.captureTime = GXDateTime()
            else:
                if isinstance(e.value, bytearray):
                    self.captureTime = _GXCommon.changeType(settings, e.value, DataType.DATETIME)
                else:
                    self.captureTime = e.value
        else:
            e.error = ErrorCode.READ_WRITE_DENIED 

def bleu(pred_tokens, label_tokens, k):
    len_pred, len_label = len(pred_tokens), len(label_tokens)
    score = math.exp(min(0, 1 - len_label / len_pred))
    for n in range(1, k + 1):
        num_matches, label_subs = 0, collections.defaultdict(int)
        for i in range(len_label - n + 1):
            label_subs[''.join(label_tokens[i: i + n])] += 1
        for i in range(len_pred - n + 1):
            if label_subs[''.join(pred_tokens[i: i + n])] > 0:
                num_matches += 1
                label_subs[''.join(pred_tokens[i: i + n])] -= 1
        score *= math.pow(num_matches / (len_pred - n + 1), math.pow(0.5, n))
    return score 

def logmgf_exact(q, priv_eps, l):
  """Computes the logmgf value given q and privacy eps.

  The bound used is the min of three terms. The first term is from
  https://arxiv.org/pdf/1605.02065.pdf.
  The second term is based on the fact that when event has probability (1-q) for
  q close to zero, q can only change by exp(eps), which corresponds to a
  much smaller multiplicative change in (1-q)
  The third term comes directly from the privacy guarantee.
  Args:
    q: pr of non-optimal outcome
    priv_eps: eps parameter for DP
    l: moment to compute.
  Returns:
    Upper bound on logmgf
  """
  if q < 0.5:
    t_one = (1-q) * math.pow((1-q) / (1 - math.exp(priv_eps) * q), l)
    t_two = q * math.exp(priv_eps * l)
    t = t_one + t_two
    try:
      log_t = math.log(t)
    except ValueError:
      print "Got ValueError in math.log for values :" + str((q, priv_eps, l, t))
      log_t = priv_eps * l
  else:
    log_t = priv_eps * l

  return min(0.5 * priv_eps * priv_eps * l * (l + 1), log_t, priv_eps * l) 

def cal_line_length(point1, point2):
    return math.sqrt( math.pow(point1[0] - point2[0], 2) + math.pow(point1[1] - point2[1], 2)) 

def train(epoch, model, source_loader, target_loader):
    #最后的全连接层学习率为前面的10倍
    LEARNING_RATE = args.lr / math.pow((1 + 10 * (epoch - 1) / args.epochs), 0.75)
    print("learning rate：", LEARNING_RATE)
    if args.diff_lr:
        optimizer = torch.optim.SGD([
        {'params': model.sharedNet.parameters()},
        {'params': model.Inception.parameters(), 'lr': LEARNING_RATE},
        ], lr=LEARNING_RATE / 10, momentum=args.momentum, weight_decay=args.l2_decay)
    else:
        optimizer = optim.SGD(model.parameters(), lr=LEARNING_RATE, momentum=args.momentum,weight_decay = args.l2_decay)
    model.train()
    tgt_iter = iter(target_loader)
    for batch_idx, (source_data, source_label) in enumerate(source_loader):
        try:
            target_data, _ = tgt_iter.next()
        except Exception as err:
            tgt_iter=iter(target_loader)
            target_data, _ = tgt_iter.next()
        
        if args.cuda:
            source_data, source_label = source_data.cuda(), source_label.cuda()
            target_data = target_data.cuda()
        optimizer.zero_grad()

        s_output, mmd_loss = model(source_data, target_data, source_label)
        soft_loss = F.nll_loss(F.log_softmax(s_output, dim=1), source_label)
        # print((2 / (1 + math.exp(-10 * (epoch) / args.epochs)) - 1))
        if args.gamma == 1:
            gamma = 2 / (1 + math.exp(-10 * (epoch) / args.epochs)) - 1
        if args.gamma == 2:
            gamma = epoch /args.epochs
        loss = soft_loss + gamma * mmd_loss
        loss.backward()
        optimizer.step()
        if batch_idx % args.log_interval == 0:
            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}\tlabel_Loss: {:.6f}\tmmd_Loss: {:.6f}'.format(
                epoch, batch_idx * len(source_data), len(train_loader.dataset),
                100. * batch_idx / len(train_loader), loss.item(), soft_loss.item(), mmd_loss.item())) 

def point_distance(p1, p2):
    """Return the distance between two points."""
    delta_x = p2.x - p1.x
    delta_y = p2.y - p1.y
    return sqrt(pow(delta_x, 2.0) + pow(delta_y, 2.0)),


# Another function used to define of one of our custom instructions. 

def point_distance(p1, p2):
    delta_x = p2.x - p1.x
    delta_y = p2.y - p1.y
    return sqrt(pow(delta_x, 2.0) + pow(delta_y, 2.0)), 

def point_distance(p1, p2):
    delta_x = p2.x - p1.x
    delta_y = p2.y - p1.y
    return sqrt(pow(delta_x, 2.0) + pow(delta_y, 2.0)) 

def pow(a, b=2):
    try:
        return math.pow(a, b)
    except OverflowError:
        return float('inf')


# Also known as logist or logistic 

def safePow(a, b):
    if a is None:
        return None
    try:
        result = math.pow(a, b)
    except ValueError:
        return None
    return result 

def calculate_pplx(self, path, val=True):
        """ Splits the input data into batches of self.args.batch_size to
        reduce the memory footprint of holding all of the data in RAM. """

        prefix = "val" if val else "test"
        logger.info("Calculating pplx over %s data", prefix)
        sum_logprobs = 0
        y_len = 0

        generator = self.data_gen.generation_generator(prefix)
        seen = 0
        for data in generator:
            Y_target = deepcopy(data[1]['output'])
            del data[1]['output']

            preds = self.model.predict(data[0],
                                       verbose=0,
                                       batch_size=self.args.batch_size)

            for i in range(Y_target.shape[0]):
                for t in range(Y_target.shape[1]):
                    target_idx = np.argmax(Y_target[i, t])
                    target_tok = self.index2word[target_idx]
                    if target_tok != "<P>":
                        log_p = math.log(preds[i, t, target_idx],2)
                        sum_logprobs += -log_p
                        y_len += 1

            seen += data[0]['text'].shape[0]
            if seen == self.data_gen.split_sizes[prefix]:
                # Hacky way to break out of the generator
                break

        norm_logprob = sum_logprobs / y_len
        pplx = math.pow(2, norm_logprob)
        logger.info("PPLX: %.4f", pplx)
        handle = open("%s/%sPPLX" % (path, prefix), "w")
        handle.write("%f\n" % pplx)
        handle.close()
        return pplx