Python numpy.var() Examples
The following are 30
code examples of numpy.var().
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Example #1
Source File: BlurDetection.py From python-- with GNU General Public License v3.0 | 10 votes |
def _lapulaseDetection(self, imgName): """ :param strdir: 文件所在的目录 :param name: 文件名称 :return: 检测模糊后的分数 """ # step1: 预处理 img2gray, reImg = self.preImgOps(imgName) # step2: laplacian算子 获取评分 resLap = cv2.Laplacian(img2gray, cv2.CV_64F) score = resLap.var() print("Laplacian %s score of given image is %s", str(score)) # strp3: 绘制图片并保存 不应该写在这里 抽象出来 这是共有的部分 newImg = self._drawImgFonts(reImg, str(score)) newDir = self.strDir + "/_lapulaseDetection_/" if not os.path.exists(newDir): os.makedirs(newDir) newPath = newDir + imgName # 显示 cv2.imwrite(newPath, newImg) # 保存图片 cv2.imshow(imgName, newImg) cv2.waitKey(0) # step3: 返回分数 return score
Example #2
Source File: BlurDetection.py From python-- with GNU General Public License v3.0 | 6 votes |
def _Variance(self, imgName): """ 灰度方差乘积 :param imgName: :return: """ # step 1 图像的预处理 img2gray, reImg = self.preImgOps(imgName) f = self._imageToMatrix(img2gray) # strp3: 绘制图片并保存 不应该写在这里 抽象出来 这是共有的部分 score = np.var(f) newImg = self._drawImgFonts(reImg, str(score)) newDir = self.strDir + "/_Variance_/" if not os.path.exists(newDir): os.makedirs(newDir) newPath = newDir + imgName cv2.imwrite(newPath, newImg) # 保存图片 cv2.imshow(imgName, newImg) cv2.waitKey(0) return score
Example #3
Source File: running_mean_std.py From lirpg with MIT License | 6 votes |
def test_runningmeanstd(): for (x1, x2, x3) in [ (np.random.randn(3), np.random.randn(4), np.random.randn(5)), (np.random.randn(3,2), np.random.randn(4,2), np.random.randn(5,2)), ]: rms = RunningMeanStd(epsilon=0.0, shape=x1.shape[1:]) x = np.concatenate([x1, x2, x3], axis=0) ms1 = [x.mean(axis=0), x.var(axis=0)] rms.update(x1) rms.update(x2) rms.update(x3) ms2 = [rms.mean, rms.var] assert np.allclose(ms1, ms2)
Example #4
Source File: running_mean_std.py From HardRLWithYoutube with MIT License | 6 votes |
def test_tf_runningmeanstd(): for (x1, x2, x3) in [ (np.random.randn(3), np.random.randn(4), np.random.randn(5)), (np.random.randn(3,2), np.random.randn(4,2), np.random.randn(5,2)), ]: rms = TfRunningMeanStd(epsilon=0.0, shape=x1.shape[1:], scope='running_mean_std' + str(np.random.randint(0, 128))) x = np.concatenate([x1, x2, x3], axis=0) ms1 = [x.mean(axis=0), x.var(axis=0)] rms.update(x1) rms.update(x2) rms.update(x3) ms2 = [rms.mean, rms.var] np.testing.assert_allclose(ms1, ms2)
Example #5
Source File: signal_changepoints.py From NeuroKit with MIT License | 6 votes |
def _signal_changepoints_cost_mean(signal): """Cost function for a normally distributed signal with a changing mean.""" i_variance_2 = 1 / (np.var(signal) ** 2) cmm = [0.0] cmm.extend(np.cumsum(signal)) cmm2 = [0.0] cmm2.extend(np.cumsum(np.abs(signal))) def cost(start, end): cmm2_diff = cmm2[end] - cmm2[start] cmm_diff = pow(cmm[end] - cmm[start], 2) i_diff = end - start diff = cmm2_diff - cmm_diff return (diff / i_diff) * i_variance_2 return cost
Example #6
Source File: standard_variance.py From TradzQAI with Apache License 2.0 | 6 votes |
def standard_variance(data, period): """ Standard Variance. Formula: (Ct - AVGt)^2 / N """ check_for_period_error(data, period) sv = list(map( lambda idx: np.var(data[idx+1-period:idx+1], ddof=1), range(period-1, len(data)) )) sv = fill_for_noncomputable_vals(data, sv) return sv
Example #7
Source File: node.py From tensortrade with Apache License 2.0 | 5 votes |
def var(self): name = "ExpandingVar({})".format(self.name) return self.agg(lambda x: np.var(x, ddof=1)).rename(name)
Example #8
Source File: node.py From tensortrade with Apache License 2.0 | 5 votes |
def std(self, bias: bool = False): name = "EWM:SD({},{})".format(self.name, self.alpha) return self.var(bias).sqrt().rename(name)
Example #9
Source File: demos.py From bayesian_bootstrap with MIT License | 5 votes |
def plot_var_bootstrap(): X = np.random.uniform(-1, 1, 100) posterior_samples = var(X, 10000) sns.distplot(posterior_samples) classical_samples = [np.var(resample(X)) for _ in range(10000)] sns.distplot(classical_samples) plt.show()
Example #10
Source File: bag_of_characters.py From sato with Apache License 2.0 | 5 votes |
def extract_bag_of_characters_features(data, n_val): characters_to_check = [ '['+ c + ']' for c in string.printable if c not in ( '\n', '\\', '\v', '\r', '\t', '^' )] + ['[\\\\]', '[\^]'] f = OrderedDict() f['n_values'] = n_val data_no_null = data.dropna() all_value_features = OrderedDict() all_value_features['length'] = data_no_null.apply(len) for c in characters_to_check: all_value_features['n_{}'.format(c)] = data_no_null.str.count(c) for value_feature_name, value_features in all_value_features.items(): f['{}-agg-any'.format(value_feature_name)] = any(value_features) f['{}-agg-all'.format(value_feature_name)] = all(value_features) f['{}-agg-mean'.format(value_feature_name)] = np.mean(value_features) f['{}-agg-var'.format(value_feature_name)] = np.var(value_features) f['{}-agg-min'.format(value_feature_name)] = np.min(value_features) f['{}-agg-max'.format(value_feature_name)] = np.max(value_features) f['{}-agg-median'.format(value_feature_name)] = np.median(value_features) f['{}-agg-sum'.format(value_feature_name)] = np.sum(value_features) f['{}-agg-kurtosis'.format(value_feature_name)] = kurtosis(value_features) f['{}-agg-skewness'.format(value_feature_name)] = skew(value_features) n_none = data.size - data_no_null.size - len([ e for e in data if e == '']) f['none-agg-has'] = n_none > 0 f['none-agg-percent'] = n_none / len(data) f['none-agg-num'] = n_none f['none-agg-all'] = (n_none == len(data)) #print(len(f)) return f
Example #11
Source File: decision_tree_regression.py From Python-Machine-Learning-By-Example-Second-Edition with MIT License | 5 votes |
def mse(targets): # When the set is empty if targets.size == 0: return 0 return np.var(targets)
Example #12
Source File: common.py From Jtyoui with MIT License | 5 votes |
def line_regression_a_b(x: np.array, y: np.array): """求解线性回归的a和β值。即y=b+a*x :param x: 数据特征值:应该是两维度。即:[[],[],[]] :param y: 数据预测值;一维度。即:[] :return: a值、和b值 """ var = np.var(x, ddof=1) # 贝塞尔校正,方差 cov = np.cov(x.transpose(), y)[0][1] # 协方差 x_ = np.mean(x) y_ = np.mean(y) a = cov / var b = y_ - a * x_ return a, b
Example #13
Source File: test_bootstrap.py From bayesian_bootstrap with MIT License | 5 votes |
def test_var_resample(self): X = np.random.uniform(-1, 1, 500) posterior_samples = bayesian_bootstrap(X, np.var, 10000, 5000, low_mem=True) self.assertAlmostEqual(np.mean(posterior_samples), 1/3., delta=0.05) X = np.random.uniform(-1, 1, 500) posterior_samples = bayesian_bootstrap(X, np.var, 10000, 5000, low_mem=False) self.assertAlmostEqual(np.mean(posterior_samples), 1 / 3., delta=0.05)
Example #14
Source File: test_bootstrap.py From bayesian_bootstrap with MIT License | 5 votes |
def test_self_covar(self): X = np.random.uniform(-1, 1, 500) posterior_samples = covar(X, X, 10000) self.assertAlmostEqual(np.mean(posterior_samples), np.var(X), delta=0.05)
Example #15
Source File: test_bootstrap.py From bayesian_bootstrap with MIT License | 5 votes |
def test_variance(self): X = np.random.uniform(-1, 1, 500) posterior_samples = var(X, 10000) self.assertAlmostEqual(np.mean(posterior_samples), 1/3., delta=0.05)
Example #16
Source File: demos.py From bayesian_bootstrap with MIT License | 5 votes |
def plot_var_resample_bootstrap(): X = np.random.uniform(-1, 1, 100) posterior_samples = bayesian_bootstrap(X, np.var, 10000, 500) sns.distplot(posterior_samples) classical_samples = [np.var(resample(X)) for _ in range(10000)] sns.distplot(classical_samples) plt.show()
Example #17
Source File: node.py From tensortrade with Apache License 2.0 | 5 votes |
def var(self, bias: bool = False): name = "EWM:Var({},{})".format(self.name, self.alpha) return ExponentialWeightedMovingVariance(bias, name)(self, self.inputs[0])
Example #18
Source File: node.py From tensortrade with Apache License 2.0 | 5 votes |
def var(self): name = "RollingVar({},{})".format(self.name, self.window) return self.agg(np.var).rename(name)
Example #19
Source File: test_nanvar.py From differential-privacy-library with MIT License | 5 votes |
def test_large_epsilon_axis(self): a = np.random.random((1000, 5)) res = np.var(a, axis=0) res_dp = nanvar(a, epsilon=1, bounds=(0, 1), axis=0) for i in range(res.shape[0]): self.assertAlmostEqual(res[i], res_dp[i], delta=0.01)
Example #20
Source File: test_nanvar.py From differential-privacy-library with MIT License | 5 votes |
def test_large_epsilon(self): a = np.random.random(1000) res = float(np.var(a)) res_dp = nanvar(a, epsilon=1, bounds=(0, 1)) self.assertAlmostEqual(res, res_dp, delta=0.01)
Example #21
Source File: test_var.py From differential-privacy-library with MIT License | 5 votes |
def test_accountant(self): from diffprivlib.accountant import BudgetAccountant acc = BudgetAccountant(1.5, 0) a = np.random.random((1000, 5)) var(a, epsilon=1, bounds=(0, 1), accountant=acc) self.assertEqual((1.0, 0), acc.total()) with acc: with self.assertRaises(BudgetError): var(a, epsilon=1, bounds=(0, 1))
Example #22
Source File: test_var.py From differential-privacy-library with MIT License | 5 votes |
def test_clipped_output(self): a = np.random.random((10,)) for i in range(100): self.assertTrue(0 <= var(a, epsilon=1e-5, bounds=(0, 1)) <= 1)
Example #23
Source File: test_var.py From differential-privacy-library with MIT License | 5 votes |
def test_array_like(self): self.assertIsNotNone(var([1, 2, 3], bounds=(1, 3))) self.assertIsNotNone(var((1, 2, 3), bounds=(1, 3)))
Example #24
Source File: test_var.py From differential-privacy-library with MIT License | 5 votes |
def test_large_epsilon_axis(self): a = np.random.random((1000, 5)) res = np.var(a, axis=0) res_dp = var(a, epsilon=1, bounds=(0, 1), axis=0) for i in range(res.shape[0]): self.assertAlmostEqual(res[i], res_dp[i], delta=0.01)
Example #25
Source File: test_var.py From differential-privacy-library with MIT License | 5 votes |
def test_large_epsilon(self): a = np.random.random(1000) res = float(np.var(a)) res_dp = var(a, epsilon=1, bounds=(0, 1)) self.assertAlmostEqual(res, res_dp, delta=0.01)
Example #26
Source File: test_var.py From differential-privacy-library with MIT License | 5 votes |
def test_missing_bounds(self): a = np.array([1, 2, 3]) with self.assertWarns(PrivacyLeakWarning): res = var(a, 1, None) self.assertIsNotNone(res)
Example #27
Source File: test_var.py From differential-privacy-library with MIT License | 5 votes |
def test_no_bounds(self): a = np.array([1, 2, 3]) with self.assertWarns(PrivacyLeakWarning): var(a, epsilon=1)
Example #28
Source File: test_var.py From differential-privacy-library with MIT License | 5 votes |
def test_no_epsilon(self): a = np.array([1, 2, 3]) self.assertIsNotNone(var(a, bounds=(0, 1)))
Example #29
Source File: test_var.py From differential-privacy-library with MIT License | 5 votes |
def test_no_params(self): a = np.array([1, 2, 3]) with self.assertWarns(PrivacyLeakWarning): res = var(a) self.assertIsNotNone(res)
Example #30
Source File: test_var.py From differential-privacy-library with MIT License | 5 votes |
def test_not_none(self): mech = var self.assertIsNotNone(mech)