datetime函数和random.seed()函数的应用

本文介绍了Python中使用datetime模块获取当前时间和日期的方法,并演示了如何利用random模块生成基于不同种子的随机数,包括如何使用当前时间作为种子来生成变化的随机数序列。

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一,datetime

在python中datetime是一个库,是一个模块,也是一个函数,作用很多,这里面只对其做简单的最常用的讲解。

首先返回系统时间

#!/usr/bin/python3

import datetime

nowTime=datetime.datetime.now()

print (nowTime)

输出结果是: 2019-04-03 14:30:02.358785

返回当天日期

Today=datetime.date.today()

print(Today)

输出的结果是:2019-04-03

时间间隔(这是一个time模块,很有用的)

#!/usr/bin/python3

import time

def sleeptime(hour,min,sec):

    return (hour*3600+min*60+sec);

sleep_time=sleeptime(0,0,3);

while 1==1:

    time.sleep(sleep_time);

    print ("每隔3秒显示一次")

输出结果是:

每隔3秒显示一次

每隔3秒显示一次

每隔3秒显示一次

 

二,random.seed()

random.seed()是随机数种子,也就是为随机数提供算法,完全相同的种子产生的随机数列是相同的,

所以如果想产生不同的随机数就需要用当前时间作为种子

#!/usr/bin/python3

import random

random.seed(0)

print ("Random number with seed 0 : ", random.random())

输出结果:Random number with seed 0 : 0.844421851525

 

#!/usr/bin/python3

import random

random.seed(0)

print ("Random number with seed 0 : ", random.random())

输出结果:Random number with seed 0 : 0.844421851525

#!/usr/bin/python3

import random

random.seed(0)

print ("Random number with seed 0 : ", random.random())

输出结果:Random number with seed 0 : 0.844421851525

 

 

以下为同时运行三个相同的随机种子

#!/usr/bin/python3

import random

random.seed(0)
print ("Random number with seed 0 : ", random.random())

random.seed(0)
print ("Random number with seed 0 : ", random.random())

random.seed(0)
print ("Random number with seed 0 : ", random.random())

输出结果:是相同的

Random number with seed 0 : 0.844421851525

Random number with seed 0 : 0.844421851525

Random number with seed 0 : 0.844421851525

 

以下为同时运行三个不同的随机种子

#!/usr/bin/python3

import random

random.seed(0)
print ("Random number with seed 0 : ", random.random())

random.seed(1)
print ("Random number with seed 0 : ", random.random())

random.seed(2)
print ("Random number with seed 0 : ", random.random())

输出结果:是不同的

Random number with seed 0 : 0.844421851525
Random number with seed 1 : 0.134364244112
Random number with seed 2 : 0.956034271889

 

所以如果想产生不同的随机数就需要用当前时间作为种子

即:

#!/usr/bin/python3

import random
import datetime

random.seed(datetime.datetime.now())
print ("Random number with当前时间: ", random.random())

输出结果:Random number with当前时间:  0.1915369729663604

#!/usr/bin/python3

import random
import datetime

random.seed(datetime.datetime.now())
print ("Random number with当前时间: ", random.random())

输出结果:Random number with当前时间:  0.26451817448470716

#!/usr/bin/python3

import random
import datetime

random.seed(datetime.datetime.now())
print ("Random number with当前时间: ", random.random())

输出结果:Random number with当前时间:  0.26258549654200924

#!/usr/bin/python3

import random
import datetime

random.seed(datetime.datetime.now())
print ("Random number with当前时间: ", random.random())

random.seed(datetime.datetime.now())
print ("Random number with当前时间: ", random.random())

random.seed(datetime.datetime.now())
print ("Random number with当前时间: ", random.random())

输出结果:结果相同

Random number with当前时间:  0.5998298461948733
Random number with当前时间:  0.5998298461948733
Random number with当前时间:  0.5998298461948733

 

总结:可以看出random.seed(datetime.datetime.now())每次输出的结果都不相同

只有在同时输出的结果才会相同,因为“同时”表明时间点是相同的

import pandas as pd import numpy as np import matplotlib.pyplot as plt from docx import Document from docx.shared import Inches from datetime import datetime # ====================== # 数据生成模块 # ====================== def generate_sales_data(): np.random.seed(42) dates = pd.date_range(start="2023-06-01", end="2023-06-30") products = [ {'name': '全麦吐司', 'cost': 5, 'price': 10}, {'name': '法棍面包', 'cost': 7, 'price': 14}, {'name': '牛角包', 'cost': 4, 'price': 8}, {'name': '甜甜圈', 'cost': 3, 'price': 6}, {'name': '黑麦面包', 'cost': 6, 'price': 12} ] # 生成温度数据(后期温度升高) temps = np.concatenate([ np.random.randint(15, 25, size=15), np.random.randint(25, 35, size=15) ]) records = [] for i, date in enumerate(dates): for p in products: # 基础销量 base_sales = np.random.randint(20, 40) # 温度影响因子 if p['name'] == '全麦吐司': sales = max(0, base_sales - int((temps[i]-20)*1.2)) elif p['name'] == '黑麦面包' and date.day >= 25: sales = 0 # 制造滞销商品 else: sales = base_sales + np.random.randint(-5, 5) records.append({ '日期': date.strftime('%Y-%m-%d'), '商品': p['name'], '销量': sales, '销售额': sales * p['price'], '成本': sales * p['cost'], '温度': temps[i] }) return pd.DataFrame(records) # ====================== # 分析计算模块 # ====================== def calculate_kpis(df): grouped = df.groupby('商品').agg( 总销售额=('销售额', 'sum'), 总成本=('成本', 'sum'), 总销量=('销量', 'sum') ) grouped['毛利率'] = (grouped['总销售额'] - grouped['总成本']) / grouped['总销售额'] grouped['周转率'] = grouped['总销量'] / 100 # 假设库存均为100件 return grouped def find_slow_moving(df): df = df.sort_values(['商品', '日期']) slow_movers = []
03-26
以下是 `umap.UMAP()` 函数的源代码: ``` class UMAP(BaseEstimator, TransformerMixin): def __init__( self, n_neighbors=15, n_components=2, metric="euclidean", metric_kwds=None, output_metric="euclidean", output_metric_kwds=None, n_epochs=None, learning_rate=1.0, init="spectral", min_dist=0.1, spread=1.0, low_memory=False, set_op_mix_ratio=1.0, local_connectivity=1.0, repulsion_strength=1.0, negative_sample_rate=5, transform_queue_size=4.0, a=None, b=None, random_state=None, angular_rp_forest=False, target_n_neighbors=-1, target_metric="categorical", target_metric_kwds=None, target_weight=0.5, transform_seed=42, force_approximation_algorithm=False, verbose=False, ): self.n_neighbors = n_neighbors self.n_components = n_components self.metric = metric self.metric_kwds = metric_kwds self.output_metric = output_metric self.output_metric_kwds = output_metric_kwds self.n_epochs = n_epochs self.learning_rate = learning_rate self.init = init self.min_dist = min_dist self.spread = spread self.low_memory = low_memory self.set_op_mix_ratio = set_op_mix_ratio self.local_connectivity = local_connectivity self.repulsion_strength = repulsion_strength self.negative_sample_rate = negative_sample_rate self.transform_queue_size = transform_queue_size self.a = a self.b = b self.random_state = random_state self.angular_rp_forest = angular_rp_forest self.target_n_neighbors = target_n_neighbors self.target_metric = target_metric self.target_metric_kwds = target_metric_kwds self.target_weight = target_weight self.transform_seed = transform_seed self.force_approximation_algorithm = force_approximation_algorithm self.verbose = verbose def fit(self, X, y=None): self.fit_transform(X, y) return self def transform(self, X): if self.transform_mode_ == "embedding": if sparse.issparse(X): raise ValueError( "Transform not available for sparse input in `" "transform_mode='embedding'`" ) X = check_array(X, dtype=np.float32, accept_sparse="csr", order="C") X -= self._a X /= self._b return self._transform(X) elif self.transform_mode_ == "graph": if not sparse.issparse(X): raise ValueError( "Transform not available for dense input in `" "transform_mode='graph'`" ) return self.graph_transform(X) else: raise ValueError("Unknown transform mode '%s'" % self.transform_mode_) def fit_transform(self, X, y=None): if self.verbose: print(str(datetime.now()), "Start fitting UMAP...") self.fit_data = X if self.output_metric_kwds is None: self.output_metric_kwds = {} if self.metric_kwds is None: self.metric_kwds = {} if sparse.isspmatrix_csr(X) and _HAVE_PYNNDESCENT: self._sparse_data = True self._knn_index = make_nn_descent( self.fit_data, self.n_neighbors, self.metric, self.metric_kwds, self.angular_rp_forest, random_state=self.random_state, low_memory=self.low_memory, verbose=self.verbose, ) else: self._sparse_data = False self._knn_index = make_nn_graph( X, n_neighbors=self.n_neighbors, algorithm="auto", metric=self.metric, metric_kwds=self.metric_kwds, angular=self.angular_rp_forest, random_state=self.random_state, verbose=self.verbose, ) # Handle small cases efficiently by computing all distances if X.shape[0] < self.n_neighbors: self._raw_data = X self.embedding_ = np.zeros((X.shape[0], self.n_components)) return self.embedding_ if self.verbose: print(str(datetime.now()), "Construct fuzzy simplicial set...") self.graph_ = fuzzy_simplicial_set( X, self.n_neighbors, random_state=self.random_state, metric=self.metric, metric_kwds=self.metric_kwds, knn_indices=self._knn_index, angular=self.angular_rp_forest, set_op_mix_ratio=self.set_op_mix_ratio, local_connectivity=self.local_connectivity, verbose=self.verbose, ) if self.verbose: print(str(datetime.now()), "Construct embedding...") self._raw_data = X if self.output_metric_kwds is None: self.output_metric_kwds = {} if self.target_n_neighbors == -1: self.target_n_neighbors = self.n_neighbors self.embedding_ = simplicial_set_embedding( self._raw_data, self.graph_, self.n_components, initial_alpha=self.learning_rate, a=self.a, b=self.b, gamma=1.0, negative_sample_rate=self.negative_sample_rate, n_epochs=self.n_epochs, init=self.init, spread=self.spread, min_dist=self.min_dist, set_op_mix_ratio=self.set_op_mix_ratio, local_connectivity=self.local_connectivity, repulsion_strength=self.repulsion_strength, metric=self.output_metric, metric_kwds=self.output_metric_kwds, verbose=self.verbose, ) self.transform_mode_ = "embedding" return self.embedding_ def graph_transform(self, X): if not sparse.issparse(X): raise ValueError( "Input must be a sparse matrix for transform with `transform_mode='graph'`" ) if self.verbose: print(str(datetime.now()), "Transform graph...") if self._sparse_data: indices, indptr, data = _sparse_knn(self._knn_index, X.indices, X.indptr, X.data) indptr = np.concatenate((indptr, [indices.shape[0]])) knn_indices, knn_dists = indices, data else: knn_indices, knn_dists = query_pairs( self._knn_index, X, self.n_neighbors, return_distance=True, metric=self.metric, metric_kwds=self.metric_kwds, angular=self.angular_rp_forest, random_state=self.random_state, verbose=self.verbose, ) graph = fuzzy_simplicial_set( X, self.n_neighbors, knn_indices=knn_indices, knn_dists=knn_dists, random_state=self.random_state, metric=self.metric, metric_kwds=self.metric_kwds, angular=self.angular_rp_forest, set_op_mix_ratio=self.set_op_mix_ratio, local_connectivity=self.local_connectivity, verbose=self.verbose, ) self.transform_mode_ = "graph" return graph def _transform(self, X): if self.verbose: print(str(datetime.now()), "Transform embedding...") if self.transform_seed is None: self.transform_seed_ = np.zeros(self.embedding_.shape[1]) else: self.transform_seed_ = self.embedding_[self.transform_seed, :].mean(axis=0) dists = pairwise_distances( X, Y=self.embedding_, metric=self.output_metric, **self.output_metric_kwds ) rng_state = np.random.RandomState(self.transform_seed_) # TODO: make binary search optional adjusted_local_connectivity = max(self.local_connectivity - 1.0, 1e-12) inv_dist = 1.0 / dists inv_dist = make_heap(inv_dist) sigmas, rhos = smooth_knn_dist( inv_dist, self.n_neighbors, local_connectivity=adjusted_local_connectivity ) rows, cols, vals = compute_membership_strengths( inv_dist, sigmas, rhos, self.negative_sample_rate, rng_state ) graph = SparseGraph( X.shape[0], self.embedding_.shape[0], rows, cols, vals, self.transform_queue_size * X.shape[0], np.random.RandomState(self.transform_seed_), self.metric, self.output_metric_kwds, self.angular_rp_forest, self.verbose, ) graph.compute_transition_matrix(self.repulsion_strength, self.epsilon) embedding = graph.compute_embedding( self.embedding_, self.learning_rate, self.n_epochs, self.min_dist, self.spread, self.init, self.set_op_mix_ratio, self._a, self._b, self.gamma, self.rp_tree_init, self.rp_tree_init_eps, self.metric, self.output_metric_kwds, self.random_state, self.verbose, ) return embedding def set_op_mix_ratio(self, mix_ratio): self.set_op_mix_ratio = mix_ratio def fuzzy_simplicial_set( X, n_neighbors, metric="euclidean", metric_kwds=None, random_state=None, knn_indices=None, angular=False, set_op_mix_ratio=1.0, local_connectivity=1.0, verbose=False, ): return fuzzy_simplicial_set( X, n_neighbors, metric=metric, metric_kwds=metric_kwds, random_state=random_state, knn_indices=knn_indices, angular=angular, set_op_mix_ratio=set_op_mix_ratio, local_connectivity=local_connectivity, verbose=verbose, ) def simplicial_set_embedding( data, graph, n_components, initial_alpha=1.0, a=None, b=None, gamma=1.0, negative_sample_rate=5, n_epochs=None, init="spectral", spread=1.0, min_dist=0.1, set_op_mix_ratio=1.0, local_connectivity=1.0, repulsion_strength=1.0, metric="euclidean", metric_kwds=None, verbose=False, ): return simplicial_set_embedding( data, graph, n_components, initial_alpha=initial_alpha, a=a, b=b, gamma=gamma, negative_sample_rate=negative_sample_rate, n_epochs=n_epochs, init=init, spread=spread, min_dist=min_dist, set_op_mix_ratio=set_op_mix_ratio, local_connectivity=local_connectivity, repulsion_strength=repulsion_strength, metric=metric, metric_kwds=metric_kwds, verbose=verbose, ) ``` 该函数实现了UMAP算法,是非常复杂的代码。简单来说,它实现了以下步骤: - 初始化UMAP对象的各种参数。 - 根据输入数据计算k近邻图,这一步可以使用pyNNDescent或BallTree算法。 - 构建模糊单纯形集,用于表示原始数据的流形结构。 - 计算新的嵌入空间,用于将原始数据降维到低维空间。 - 支持transform方法,以便在已经学习了嵌入空间之后将新的数据映射到该空间中。 - 支持fuzzy_simplicial_setsimplicial_set_embedding方法,以便使用UMAP算法的不同组件。
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