小编use*_*352的帖子

有什么方法可以在 neo4j 中创建超图吗？或者有其他数据库工具可以解决这个问题吗？

mean_sqr = tf.reduce_mean(tf.pow(y_ - y, 2))
optimizer = tf.train.AdamOptimizer(LEARNING_RATE)
gradients, variables = zip(*optimizer.compute_gradients(mean_sqr))
opt = optimizer.apply_gradients(list(zip(gradients, variables)))

init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)

for j in range(TRAINING_EPOCHS):
    sess.run(opt, feed_dict={x: batch_xs, y_: batch_xs})

我不太清楚compute_gradients会返回什么？它是否为batch_xs分配的给定x值返回sum（dy / dx），并更新apply_gradients函数中的梯度，例如：
theta < -theta -LEARNING_RATE * 1 / m * gradients？

还是已经返回给定批次中每个x值求和的梯度平均值，例如sum（dy / dx）* 1 / m，m定义为batch_size？

在此链接中，给出了两个概率分布之间的总变异距离。

我尝试用python计算它。我有两个数据集，首先我根据直方图计算了它们的概率分布函数。然后我尝试获得两个分布之间的最大差异。但它返回给我的值非常小。看来我在这方面做错了。你能帮忙修复一下吗？

import scipy.stats as st
#original data has shape of [45222,1] and it is numpy array
#synthetic data has shape of [45222,1] and it is numpy array
summation = 0
minOriginal = min(original)
minGenerated = min(synthetic)

maxOriginal = max(original)
maxGenerated = max(synthetic)

minHist = min(minOriginal, minGenerated)
maxHist = max(maxOriginal, maxGenerated)

originalHist = np.histogram(original, range=(minHist, maxHist))
hist_dist1 = st.rv_histogram(originalHist)

generatedHist = np.histogram(synthetic, range=(minHist, maxHist))
hist_dist2 = st.rv_histogram(generatedHist)

x = np.linspace(minHist, maxHist, 45000)
summation += max(abs(hist_dist1.pdf(x)-hist_dist2.pdf(x)))

在这里，我尝试从 python 代码调用 R 函数。

import numpy as np
import pandas as pd
import rpy2.robjects as ro
import rpy2.robjects.numpy2ri
rpy2.robjects.numpy2ri.activate()

time_series_data = pd.read_csv(daily_file_path, sep=",", header=None).values
nr,nc = time_series_data.shape
r_time_series_data = ro.r.matrix(time_series_data, nrow=nr, ncol=nc)
ro.r.assign("r_time_series_data", r_time_series_data)

当我导入 R 库时，它会引发以下错误：

Traceback (most recent call last):
  File "C:\Program Files\JetBrains\PyCharm Edu 4.0\helpers\pydev\pydevd.py", line 1596, in <module>
    globals = debugger.run(setup['file'], None, None, is_module)
  File "C:\Program Files\JetBrains\PyCharm Edu 4.0\helpers\pydev\pydevd.py", line 1023, in run
    pydev_imports.execfile(file, globals, locals)  # execute the script
  File "C:\Program Files\JetBrains\PyCharm Edu 4.0\helpers\pydev\_pydev_imps\_pydev_execfile.py", line …

我需要从数据集中为回归任务选择一些特征。但是数值来自不同的范围。

from sklearn.datasets import load_boston
from sklearn.feature_selection import SelectKBest, f_regression

X, y = load_boston(return_X_y=True)
X_new = SelectKBest(f_regression, k=2).fit_transform(X, y)

为了提高回归模型的性能，我是否需要在SelectKBest方法之前对 X 进行归一化？

我对时间序列分析有疑问。我有一个包含 5 个特征的数据集。以下是我的输入数据集的子集：

date,price,year,day,totaltx
1/1/2016 0:00,434.46,2016,1,126762
1/2/2016 0:00,433.59,2016,2,147449
1/3/2016 0:00,430.36,2016,3,148661
1/4/2016 0:00,433.49,2016,4,185279
1/5/2016 0:00,432.25,2016,5,178723
1/6/2016 0:00,429.46,2016,6,184207

我的内源数据是价格列，外源数据是totaltx价格。

这是我正在运行并收到错误的代码：

import statsmodels.api as sm
import pandas as pd
import numpy as np
from numpy.linalg import LinAlgError

def arima(filteredData, coinOutput, window, horizon, trainLength):
    start_index = 0
    end_index = 0
    inputNumber = filteredData.shape[0]
    predictions = np.array([], dtype=np.float32)
    prices = np.array([], dtype=np.float32)
    # sliding on time series data with 1 day step
    while ((end_index) < inputNumber - 1):
        end_index = start_index + trainLength
        trainFeatures = …

我有一段代码使用Sigmoid激活函数进行分类，以输出[0,1]。但是我需要一个激活函数来输出0或1的二进制值。

        x = tf.placeholder("float", [None, COLUMN])
        Wh = tf.Variable(tf.random_normal([COLUMN, UNITS_OF_HIDDEN_LAYER], mean=0.0, stddev=0.05))
        h = tf.nn.sigmoid(tf.matmul(x, Wh))

        Wo = tf.Variable(tf.random_normal([UNITS_OF_HIDDEN_LAYER, COLUMN], mean=0.0, stddev=0.05))
        y = tf.nn.sigmoid(tf.matmul(h, Wo))

        # Objective functions
        y_ = tf.placeholder("float", [None, COLUMN])
        correct_prediction = tf.equal(tf.argmax(y, 1),tf.argmax(y, 1))
        cost = tf.reduce_sum(tf.cast(correct_prediction, "float"))/BATCH_SIZE

您能告诉我如何用二进制第一步代替Sigmoid函数吗？