小编had*_*dim的帖子

我想从我的DataFrame中的列修改一些值.目前,我通过原始的多索引从select中查看df(并且修改确实发生了变化df).

这是一个例子:

In [1]: arrays = [np.array(['bar', 'bar', 'baz', 'qux', 'qux', 'bar']),
                  np.array(['one', 'two', 'one', 'one', 'two', 'one']),
                  np.arange(0, 6, 1)]
In [2]: df = pd.DataFrame(randn(6, 3), index=arrays, columns=['A', 'B', 'C'])

In [3]: df
                  A         B         C
bar one 0 -0.088671  1.902021 -0.540959
    two 1  0.782919 -0.733581 -0.824522
baz one 2 -0.827128 -0.849712  0.072431
qux one 3 -0.328493  1.456945  0.587793
    two 4 -1.466625  0.720638  0.976438
bar one 5 -0.456558  1.163404  0.464295

我尝试将片段修改df为标量值:

In …

我tf.estimator.Estimator用来训练模型:

def model_fn(features, labels, mode, params, config):

    input_image = features["input_image"]

    eval_metric_ops = {}
    predictions = {}

    # Create model
    with tf.name_scope('Model'):

        W = tf.Variable(tf.zeros([784, 10]), name="W")
        b = tf.Variable(tf.zeros([10]), name="b")
        logits = tf.nn.softmax(tf.matmul(input_image, W, name="MATMUL") + b, name="logits")

    loss = None
    train_op = None

    if mode != tf.estimator.ModeKeys.PREDICT:
        loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=labels, logits=logits))
        train_op = tf.contrib.layers.optimize_loss(loss=loss,
                                                       global_step=tf.contrib.framework.get_global_step(),
                                                       learning_rate=params["learning_rate"],
                                                       optimizer=params["optimizer"])
    # Add prediction
    classes = tf.as_string(tf.argmax(input=logits, axis=1, name="class"))
    with tf.name_scope('Predictions'):
        predictions["logits"] = logits
        predictions["classes"] = classes

    export_outputs = {"classes": tf.estimator.export.ClassificationOutput(classes=classes)} …

这是对社区的一个呼吁,看看是否有人有想法提高这个MSD计算实施的速度.它主要基于此博客文章的实现:http://damcb.com/mean-square-disp.html

目前,对于500个点的2D轨迹,当前实施需要大约9秒.如果你需要计算很多轨迹,这真的太过分了......

我没有尝试并行化(使用multiprocess或者joblib),但我觉得创建新进程对于这种算法来说太重了.

这是代码:

import os

import matplotlib
import matplotlib.pyplot as plt

import pandas as pd
import numpy as np

# Parameters
N = 5000
max_time = 100
dt = max_time / N

# Generate 2D brownian motion

t = np.linspace(0, max_time, N)
xy = np.cumsum(np.random.choice([-1, 0, 1], size=(N, 2)), axis=0)
traj = pd.DataFrame({'t': t, 'x': xy[:,0], 'y': xy[:,1]})
print(traj.head())

# Draw motion
ax = traj.plot(x='x', y='y', alpha=0.6, legend=False)

# Set limits
ax.set_xlim(traj['x'].min(), …