暑假更新
This commit is contained in:
dingjiawen@xiaomi.com
kevinding1125
parent
1d7bfec0f8
commit
b397a6bd82
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@ -0,0 +1,10 @@
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package com.atguigu.scala
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class AAA(bbb:BBB) {
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def open(): Unit = {
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bbb.open()
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println("aaaOpen")
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}
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}
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@ -0,0 +1,15 @@
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package com.atguigu.scala
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class BBB {
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def this(a:Int,b:Int) {
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this()
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println(a)
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println(b)
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}
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def open() ={
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println("open")
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}
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}
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@ -0,0 +1,9 @@
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package com.atguigu.scala
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object test111 {
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def main(args: Array[String]): Unit = {
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new AAA(new BBB(1,2)).open()
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}
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}
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@ -2,7 +2,11 @@ package com.markilue.leecode;
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import cn.hutool.json.JSONObject;
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import cn.hutool.json.JSONUtil;
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import com.cqu.phmapiclientsdk.client.PhmApiClient;
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import java.time.LocalDate;
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import java.time.LocalDateTime;
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import java.time.format.DateTimeFormatter;
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/**
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*@BelongsProject: Leecode
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@ -15,9 +19,9 @@ import com.cqu.phmapiclientsdk.client.PhmApiClient;
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public class Test1 {
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public static void main(String[] args) {
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PhmApiClient phmApiClient = new PhmApiClient("875f0554b4e041b3ba4be50cb68eb94f", "438fdae618794937a5333cfb6856a601");
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JSONObject jsonObject = phmApiClient.invokeByURL("http://172.28.9.61:8090/api/gas-turbine/things/search/", "{\"page_index\": 1, \"page_size\": 10, \"thing_group_uuid\":\"c1b3eb711fbd4475b262fe84fc965ea1\"}", "POST");
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String s = JSONUtil.toJsonStr(jsonObject);
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System.out.println(s);
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String dateString = "20230822";
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DateTimeFormatter formatter = DateTimeFormatter.ofPattern("yyyyMMdd");
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LocalDate dateTime = LocalDate.parse(dateString, formatter);
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System.out.println("Parsed LocalDateTime: " + dateTime);
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}
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}
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@ -16,7 +16,7 @@ public class Fibonaqi {
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public static void main(String[] args) {
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int n = 5;
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System.out.println(fibonacci(1, 1, 10));
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Arrays.copyOf()
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}
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public static int fibonacci(int first, int second, int n) {
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@ -0,0 +1,117 @@
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#! -*- coding: utf-8 -*-
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# 线性循环单元（Linear Recurrent Unit）
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# tensorflow 1.15 + bert4keras 0.11.4 测试通过
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from bert4keras.layers import *
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class LRU(Layer):
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"""线性循环单元
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链接1：https://arxiv.org/abs/2303.06349
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链接2：https://kexue.fm/archives/9554
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"""
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def __init__(
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self,
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units,
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activation='linear',
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use_bias=True,
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unroll=True, # unroll可以加速训练，但是会增加显存消耗
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kernel_initializer='glorot_uniform',
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**kwargs
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):
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super(LRU, self).__init__(**kwargs)
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self.units = units
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self.activation = activations.get(activation)
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self.use_bias = use_bias
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self.unroll = unroll
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self.kernel_initializer = initializers.get(kernel_initializer)
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@integerize_shape
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def build(self, input_shape):
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super(LRU, self).build(input_shape)
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hidden_size = input_shape[-1]
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self.i_dense = Dense(
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units=self.units * 2,
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use_bias=self.use_bias,
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kernel_initializer=self.kernel_initializer
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)
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self.o_dense = Dense(
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units=hidden_size,
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use_bias=self.use_bias,
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activation=self.activation,
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kernel_initializer=self.kernel_initializer
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)
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def initializer(shape, dtype=None):
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r_min, r_max = 0.9, 0.999
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u1 = np.random.random(size=shape[1])
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u2 = np.random.random(size=shape[1])
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nu_log = np.log(
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-0.5 * np.log(u1 * (r_max**2 - r_min**2) + r_min**2)
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)
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theta_log = np.log(u2 * np.pi * 2)
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gamma_log = np.log(np.sqrt(1 - np.exp(-np.exp(nu_log))**2))
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return np.array([nu_log, theta_log, gamma_log])
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self.params_log = self.add_weight(
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name='params_log', shape=(3, self.units), initializer=initializer
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)
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@recompute_grad
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def call(self, inputs, mask=None):
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u = self.i_dense(inputs)
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params = K.exp(self.params_log)
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nu, theta, gamma = params[0], params[1], params[2]
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if self.unroll:
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L_in = K.int_shape(u)[1]
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assert L_in is not None, 'input_length can not be None while unroll=True'
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log2_L = int(np.ceil(np.log2(L_in)))
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else:
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L_in = K.shape(u)[1]
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log2_L = K.log(K.cast(L_in, K.floatx())) / K.log(2.)
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log2_L = K.cast(tf.ceil(log2_L), 'int32')
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u = tf.complex(u[..., ::2], u[..., 1::2])
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u = tf.pad(u, [[0, 0], [0, 2**log2_L - K.shape(u)[1]], [0, 0]])
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B, L, D = K.shape(u)[0], K.shape(u)[1], K.int_shape(u)[-1]
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def lru(i, x):
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l = 2**i
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x = K.reshape(x, [B * L // l, l, D])
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x1, x2 = x[:, :l // 2], x[:, l // 2:]
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pos = K.arange(1, l // 2 + 1, dtype=K.floatx())
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nus = tf.einsum('n,d->nd', pos, nu)
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thetas = tf.einsum('n,d->nd', pos, theta)
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lambs = K.exp(tf.complex(-nus, thetas))
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x2 = x2 + lambs * x1[:, -1:]
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x = K.concatenate([x1, x2], axis=1)
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if (not self.unroll) and K.int_shape(u)[1] is not None:
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x = K.reshape(x, [B, L, D])
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return i + 1, x
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if self.unroll:
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x = u
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for i in range(log2_L):
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_, x = lru(i + 1, x)
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else:
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_, x = tf.while_loop(lambda i, x: i <= log2_L, lru, [1, u])
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x = x[:, :L_in] * tf.complex(gamma, 0.)
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x = K.concatenate([tf.real(x), tf.imag(x)], axis=-1)
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return self.o_dense(x)
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def get_config(self):
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config = {
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'units': self.units,
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'activation': activations.serialize(self.activation),
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'use_bias': self.use_bias,
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'unroll': self.unroll,
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'kernel_initializer':
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initializers.serialize(self.kernel_initializer),
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}
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base_config = super(LRU, self).get_config()
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return dict(list(base_config.items()) + list(config.items()))
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@ -0,0 +1,113 @@
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import numpy as np
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import tensorflow as tf
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from tensorflow.keras.layers import Layer, Dense, activations, initializers
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from tensorflow.keras import backend as K
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class LRU(Layer):
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"""线性循环单元
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链接1：https://arxiv.org/abs/2303.06349
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链接2：https://kexue.fm/archives/9554
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"""
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def __init__(
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self,
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units,
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activation='linear',
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use_bias=True,
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unroll=True, # unroll可以加速训练，但是会增加显存消耗
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kernel_initializer='glorot_uniform',
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**kwargs
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):
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super(LRU, self).__init__(**kwargs)
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self.units = units
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self.activation = activations.get(activation)
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self.use_bias = use_bias
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self.unroll = unroll
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self.kernel_initializer = initializers.get(kernel_initializer)
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def build(self, input_shape):
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super(LRU, self).build(input_shape)
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hidden_size = input_shape[-1]
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self.i_dense = Dense(
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units=self.units * 2,
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use_bias=self.use_bias,
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kernel_initializer=self.kernel_initializer
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)
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self.o_dense = Dense(
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units=hidden_size,
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use_bias=self.use_bias,
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activation=self.activation,
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kernel_initializer=self.kernel_initializer
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)
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def initializer(shape, dtype=None):
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r_min, r_max = 0.9, 0.999
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u1 = np.random.random(size=shape[1])
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u2 = np.random.random(size=shape[1])
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nu_log = np.log(
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-0.5 * np.log(u1 * (r_max**2 - r_min**2) + r_min**2)
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)
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theta_log = np.log(u2 * np.pi * 2)
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gamma_log = np.log(np.sqrt(1 - np.exp(-np.exp(nu_log))**2))
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return np.array([nu_log, theta_log, gamma_log])
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self.params_log = self.add_weight(
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name='params_log', shape=(3, self.units), initializer=initializer
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)
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@tf.function
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def call(self, inputs, mask=None):
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u = self.i_dense(inputs)
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params = tf.exp(self.params_log)
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nu, theta, gamma = params[0], params[1], params[2]
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if self.unroll:
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L_in = K.int_shape(u)[1]
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assert L_in is not None, 'input_length can not be None while unroll=True'
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log2_L = tf.cast(tf.math.ceil(tf.math.log(L_in) / tf.math.log(2.)), tf.int32)
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else:
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L_in = tf.shape(u)[1]
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log2_L = tf.cast(tf.math.ceil(tf.math.log(tf.cast(L_in, dtype=tf.float32)) / tf.math.log(2.)), tf.int32)
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u = tf.complex(u[..., ::2], u[..., 1::2])
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u = tf.pad(u, [[0, 0], [0, 2**log2_L - tf.shape(u)[1]], [0, 0]])
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B, L, D = tf.shape(u)[0], tf.shape(u)[1], tf.shape(u)[-1]
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def lru(i, x):
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l = 2**i
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x = tf.reshape(x, [B * L // l, l, D])
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x1, x2 = x[:, :l // 2], x[:, l // 2:]
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pos = tf.range(1, l // 2 + 1, dtype=tf.float32)
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nus = tf.einsum('n,d->nd', pos, nu)
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thetas = tf.einsum('n,d->nd', pos, theta)
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lambs = tf.exp(tf.complex(-nus, thetas))
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x2 = x2 + lambs * x1[:, -1:]
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x = tf.concat([x1, x2], axis=1)
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if (not self.unroll) and tf.shape(u)[1] is not None:
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x = tf.reshape(x, [B, L, D])
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return i + 1, x
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if self.unroll:
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x = u
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for i in range(log2_L):
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_, x = lru(i + 1, x)
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else:
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_, x = tf.while_loop(lambda i, x: i <= log2_L, lru, [1, u])
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x = x[:, :L_in] * tf.complex(gamma, 0.)
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x = tf.concat([tf.math.real(x), tf.math.imag(x)], axis=-1)
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return self.o_dense(x)
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def get_config(self):
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config = {
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'units': self.units,
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'activation': activations.serialize(self.activation),
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'use_bias': self.use_bias,
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'unroll': self.unroll,
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'kernel_initializer':
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initializers.serialize(self.kernel_initializer),
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}
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base_config = super(LRU, self).get_config()
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return dict(list(base_config.items()) + list(config.items()))
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@ -0,0 +1,41 @@
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package com.markilue.interview;
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import java.util.Arrays;
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import java.util.Scanner;
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/**
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*@BelongsProject: MeiTuan
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*@BelongsPackage: com.markilue.interview
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*@Author: markilue
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*@CreateTime: 2023-09-16 19:01
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*@Description: TODO
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*@Version: 1.0
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*/
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public class Question1 {
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public static void main(String[] args) {
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Scanner sc = new Scanner(System.in);
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int n = sc.nextInt();
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int[] nums = new int[n];
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for (int i = 0; i < n; i++) {
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nums[i]=sc.nextInt();
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}
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solve(nums);
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}
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public static void solve(int[] nums){
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int result =nums[0]==1?1:0;
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for (int i = 1; i < nums.length; i++) {
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if(nums[i]==1){
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result+=nums[i-1]==1?2:1;
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}
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}
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System.out.println(result);
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}
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}
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@ -0,0 +1,38 @@
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package com.markilue.interview;
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import java.util.Scanner;
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/**
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*@BelongsProject: MeiTuan
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*@BelongsPackage: com.markilue.interview
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*@Author: markilue
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*@CreateTime: 2023-09-16 19:06
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*@Description: TODO
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*@Version: 1.0
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*/
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public class Question2 {
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public static void main(String[] args) {
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Scanner sc = new Scanner(System.in);
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int x = sc.nextInt();
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int y = sc.nextInt();
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// if(x>10||y>10){
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// System.out.println(0);
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// return;
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// }
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double dinct = Math.ceil(Math.sqrt(x * x + y * y));
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if (dinct == 0) {
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System.out.println(10);
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} else if (dinct > 10) {
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System.out.println(0);
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} else {
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System.out.println((int)(11 - dinct));
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}
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}
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}
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@ -0,0 +1,126 @@
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package com.markilue.interview;
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import org.junit.Test;
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import java.util.Arrays;
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import java.util.Comparator;
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import java.util.Scanner;
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/**
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*@BelongsProject: MeiTuan
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*@BelongsPackage: com.markilue.interview
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*@Author: markilue
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*@CreateTime: 2023-09-16 19:20
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*@Description: TODO
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*@Version: 1.0
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*/
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public class Question3 {
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public static void main(String[] args) {
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Scanner sc = new Scanner(System.in);
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int n = sc.nextInt();
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int targetH = sc.nextInt();
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int targetA = sc.nextInt();
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int[][] monster = new int[n][2];
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for (int i = 0; i < n; i++) {
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monster[i][0] = sc.nextInt();
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}
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for (int i = 0; i < n; i++) {
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monster[i][1] = sc.nextInt();
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}
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sovle(targetH,targetA,monster);
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}
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@Test
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public void test() {
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int[][] monster = {
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{1, 3},
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{2, 2},
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{3, 1},
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};
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sovle(1, 1, monster);
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}
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public static void sovle(int targetH, int targetA, int[][] monster) {
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Arrays.sort(monster, new Comparator<int[]>() {
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@Override
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public int compare(int[] o1, int[] o2) {
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return o1[0] == o2[0] ? o2[1] - o1[1] : o2[0] - o1[0];
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}
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});
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int[][] amons = Arrays.copyOf(monster, monster.length);
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Arrays.sort(amons, (a, b) -> (b[1] - a[1]));
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int leftans = 0;
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int righttans = 0;
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int count1 = 0;
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int count2 = 0;
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int i = 0;
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while (targetH <= monster[i][0]) {
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i++;
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if (i == monster.length) break;
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}
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||||
|
||||
for (int j = i; j < monster.length; j++) {
|
||||
count2=0;
|
||||
if(targetA>monster[j][1]){
|
||||
int temp =1;
|
||||
int k=j+1;
|
||||
for (;k<monster.length;k++){
|
||||
if(monster[k][1]<monster[k-1][1])temp++;
|
||||
else {
|
||||
count2=Math.max(count2,temp);
|
||||
break;
|
||||
}
|
||||
}
|
||||
}
|
||||
count1=monster.length-j;
|
||||
leftans=Math.max(leftans,Math.min(count1,count2));
|
||||
}
|
||||
i=0;
|
||||
count1=0;
|
||||
count2=0;
|
||||
while (targetA<=amons[i][1]){
|
||||
i++;
|
||||
if(i==monster.length)break;
|
||||
}
|
||||
count1=monster.length-i;
|
||||
for (int j = i; j < monster.length; j++) {
|
||||
count2=0;
|
||||
if(targetH>amons[j][0]){
|
||||
int temp =1;
|
||||
int k=j+1;
|
||||
for (;k<monster.length;k++){
|
||||
if(amons[k][0]<amons[k-1][0])temp++;
|
||||
else {
|
||||
count2=Math.max(count2,temp);
|
||||
break;
|
||||
}
|
||||
}
|
||||
}
|
||||
count1=monster.length-j;
|
||||
righttans=Math.max(righttans,Math.min(count1,count2));
|
||||
}
|
||||
|
||||
|
||||
righttans=Math.min(count1,count2);
|
||||
System.out.println(Math.max(leftans,righttans));
|
||||
|
||||
|
||||
|
||||
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,55 @@
|
|||
package com.markilue.interview;
|
||||
|
||||
import org.junit.Test;
|
||||
|
||||
import java.util.Scanner;
|
||||
|
||||
/**
|
||||
*@BelongsProject: MeiTuan
|
||||
*@BelongsPackage: com.markilue.interview
|
||||
*@Author: markilue
|
||||
*@CreateTime: 2023-09-16 19:45
|
||||
*@Description: TODO
|
||||
*@Version: 1.0
|
||||
*/
|
||||
public class Question4 {
|
||||
|
||||
|
||||
public static void main(String[] args) {
|
||||
Scanner sc = new Scanner(System.in);
|
||||
int n = sc.nextInt();
|
||||
long[] nums = new long[n];
|
||||
for (int i = 0; i < n; i++) {
|
||||
nums[i] = sc.nextLong();
|
||||
}
|
||||
|
||||
sovle(nums);
|
||||
}
|
||||
|
||||
@Test
|
||||
public void test() {
|
||||
|
||||
// System.out.println(Math.log(4)/Math.log(2));
|
||||
long[] nums = {1, 2, 3, 20, 28};
|
||||
sovle(nums);
|
||||
|
||||
|
||||
}
|
||||
|
||||
public static void sovle(long[] nums) {
|
||||
|
||||
long max = 0;
|
||||
|
||||
for (long num : nums) {
|
||||
long temp = num & (num - 1);
|
||||
if (temp == 0) {
|
||||
max = Math.max(max, (long) (Math.log(num) / Math.log(2)));
|
||||
} else {
|
||||
max = Math.max(max, (long) (Math.log(num - temp) / Math.log(2)));
|
||||
}
|
||||
}
|
||||
|
||||
System.out.println(max);
|
||||
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,79 @@
|
|||
package com.markilue.interview;
|
||||
|
||||
import org.junit.Test;
|
||||
|
||||
import java.util.Scanner;
|
||||
|
||||
/**
|
||||
*@BelongsProject: MeiTuan
|
||||
*@BelongsPackage: com.markilue.interview
|
||||
*@Author: markilue
|
||||
*@CreateTime: 2023-09-16 19:45
|
||||
*@Description: TODO
|
||||
*@Version: 1.0
|
||||
*/
|
||||
public class Question5 {
|
||||
|
||||
|
||||
static int[] father;
|
||||
|
||||
public static void main(String[] args) {
|
||||
Scanner sc = new Scanner(System.in);
|
||||
int n = sc.nextInt();
|
||||
father = new int[n];
|
||||
|
||||
int m = sc.nextInt();
|
||||
|
||||
for (int i = 0; i < m; i++) {
|
||||
int a =sc.nextInt();
|
||||
int b =sc.nextInt();
|
||||
int count =sc.nextInt();
|
||||
int need =sc.nextInt();
|
||||
if(!isConnect(a,b)){
|
||||
connect(a,b);
|
||||
}
|
||||
}
|
||||
|
||||
System.out.println(-1);
|
||||
}
|
||||
|
||||
//是否连通
|
||||
public static int connect(int i, int j) {
|
||||
if (father[i] == j) {
|
||||
return j;
|
||||
}
|
||||
father[i] = connect(father[i], j);
|
||||
|
||||
return father[i];
|
||||
|
||||
}
|
||||
|
||||
//是否连通
|
||||
public static boolean isConnect(int i, int j) {
|
||||
int a = father[i];
|
||||
int b = father[j];
|
||||
if (a == b) {
|
||||
return true;
|
||||
}
|
||||
return false;
|
||||
|
||||
}
|
||||
|
||||
|
||||
public static void sovle(long[] nums) {
|
||||
|
||||
long max = 0;
|
||||
|
||||
for (long num : nums) {
|
||||
long temp = num & (num - 1);
|
||||
if (temp == 0) {
|
||||
max = Math.max(max, (long) (Math.log(num) / Math.log(2)));
|
||||
} else {
|
||||
max = Math.max(max, (long) (Math.log(num - temp) / Math.log(2)));
|
||||
}
|
||||
}
|
||||
|
||||
System.out.println(max);
|
||||
|
||||
}
|
||||
}
|
||||
Loading…
Reference in New Issue