Python math.pow() Examples
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Example #1
Source Project: TiebaTool Author: ZRStea File: run.py License: MIT License | 8 votes |
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
Example #2
Source Project: unicorn-hat-hd Author: pimoroni File: demo.py License: MIT License | 6 votes |
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
Example #3
Source Project: Gurux.DLMS.Python Author: Gurux File: GXDLMSRegister.py License: GNU General Public License v2.0 | 6 votes |
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
Example #4
Source Project: EDeN Author: fabriziocosta File: graph_layout.py License: MIT License | 6 votes |
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
Example #5
Source Project: BiblioPixelAnimations Author: ManiacalLabs File: bloom.py License: MIT License | 6 votes |
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)]
Example #6
Source Project: CAMISIM Author: CAMI-challenge File: validator.py License: Apache License 2.0 | 6 votes |
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)
Example #7
Source Project: dynamic-training-with-apache-mxnet-on-aws Author: awslabs File: text8_data.py License: Apache License 2.0 | 6 votes |
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
Example #8
Source Project: dynamic-training-with-apache-mxnet-on-aws Author: awslabs File: misc.py License: Apache License 2.0 | 6 votes |
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
Example #9
Source Project: AdaptiveWingLoss Author: protossw512 File: utils.py License: Apache License 2.0 | 6 votes |
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
Example #10
Source Project: CSD-SSD Author: soo89 File: train_csd.py License: MIT License | 6 votes |
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
Example #11
Source Project: worker Author: moira-alert File: functions.py License: GNU General Public License v3.0 | 6 votes |
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)
Example #12
Source Project: hadrian Author: modelop File: spec.py License: Apache License 2.0 | 6 votes |
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
Example #13
Source Project: VTuber_Unity Author: kwea123 File: utils.py License: MIT License | 6 votes |
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
Example #14
Source Project: sabre Author: UMass-LIDS File: sabre-mmsys18.py License: BSD 2-Clause "Simplified" License | 6 votes |
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
Example #15
Source Project: sabre Author: UMass-LIDS File: sabre-tomm19.py License: BSD 2-Clause "Simplified" License | 6 votes |
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
Example #16
Source Project: sabre Author: UMass-LIDS File: sabre.py License: BSD 2-Clause "Simplified" License | 6 votes |
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
Example #17
Source Project: holodeck Author: BYU-PCCL File: util.py License: MIT License | 6 votes |
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])
Example #18
Source Project: BiblioPixel Author: ManiacalLabs File: util.py License: MIT License | 6 votes |
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)]
Example #19
Source Project: unicorn-hat-hd Author: pimoroni File: demo.py License: MIT License | 5 votes |
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)
Example #20
Source Project: Gurux.DLMS.Python Author: Gurux File: GXDLMSExtendedRegister.py License: GNU General Public License v2.0 | 5 votes |
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
Example #21
Source Project: deep-learning-note Author: wdxtub File: 53_machine_translation.py License: MIT License | 5 votes |
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
Example #22
Source Project: DOTA_models Author: ringringyi File: analysis.py License: Apache License 2.0 | 5 votes |
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)
Example #23
Source Project: DOTA_models Author: ringringyi File: utils.py License: Apache License 2.0 | 5 votes |
def cal_line_length(point1, point2): return math.sqrt( math.pow(point1[0] - point2[0], 2) + math.pow(point1[1] - point2[1], 2))
Example #24
Source Project: transferlearning Author: jindongwang File: MRAN.py License: MIT License | 5 votes |
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()))
Example #25
Source Project: pyshgp Author: erp12 File: point_distance.py License: MIT License | 5 votes |
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.
Example #26
Source Project: pyshgp Author: erp12 File: conftest.py License: MIT License | 5 votes |
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)),
Example #27
Source Project: pyshgp Author: erp12 File: test_estimator.py License: MIT License | 5 votes |
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))
Example #28
Source Project: python-esppy Author: sassoftware File: jmp_score.py License: Apache License 2.0 | 5 votes |
def pow(a, b=2): try: return math.pow(a, b) except OverflowError: return float('inf') # Also known as logist or logistic
Example #29
Source Project: worker Author: moira-alert File: functions.py License: GNU General Public License v3.0 | 5 votes |
def safePow(a, b): if a is None: return None try: result = math.pow(a, b) except ValueError: return None return result
Example #30
Source Project: GroundedTranslation Author: elliottd File: generate.py License: BSD 3-Clause "New" or "Revised" License | 5 votes |
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