一個運算的方法到底要定義為類別方法或物件方法,完全取決於設計者。 底下以簡單的整數加法運算為例,說明定義為類別方法或物件方法時,製作及使用上的差別。 一般是類別設計者先決定運算要如何使用,再決定如何製作。 四種設計關鍵在於: 類別方法或物件方法 vs 結果回傳或覆蓋第1數 (1) 類別方法,回傳結果 public class WholeNumber { public int number; public WholeNumber(int n) { this.number = n; } public String toString() { return String.format("%d", number); } public static WholeNumber add(WholeNumber n1, WholeNumber n2) { WholeNumber n = new WholeNumber(0); n.number = n1.number + n2.number; return n; } // n3:7 = n1:3 + n2:4 public static void main(String args[]) { WholeNumber n1 = new WholeNumber(3); WholeNumber n2 = new WholeNumber(4); WholeNumber n3 = WholeNumber.add(n1, n2); System.out.printf("n3:%s = n1:%s + n2:%s\n", n3, n1, n2); } } (2) 類別方法,結果覆蓋第1數 public class WholeNumber { public int number; public WholeNumber(int n) { this.number = n; } public String toString() { return String.format("%d", number); } public static void add(WholeNumber n1, WholeNumber n2) { WholeNumber n = new WholeNumber(0); n.number = n1.number + n2.number; n1.number = n.number; } // n1:7 = n1:3 + n2:4 public static void main(String args[]) { WholeNumber n1 = new WholeNumber(3); WholeNumber n2 = new WholeNumber(4); WholeNumber n1_copy = new WholeNumber(3); WholeNumber.add(n1, n2); System.out.printf("n1:%s = n1:%s + n2:%s\n", n1, n1_copy, n2); } } (3) 物件方法,回傳結果 public class WholeNumber { public int number; public WholeNumber(int n) { this.number = n; } public String toString() { return String.format("%d", number); } public WholeNumber add(WholeNumber n2) { WholeNumber n = new WholeNumber(0); n.number = this.number + n2.number; return n; } // n3:7 = n1:3 + n2:4 public static void main(String args[]) { WholeNumber n1 = new WholeNumber(3); WholeNumber n2 = new WholeNumber(4); WholeNumber n3 = n1.add(n2); System.out.printf("n3:%s = n1:%s + n2:%s\n", n3, n1, n2); } } (4) 物件方法,結果覆蓋第1數 public class WholeNumber { public int number; public WholeNumber(int n) { this.number = n; } public String toString() { return String.format("%d", number); } public void add(WholeNumber n2) { WholeNumber n = new WholeNumber(0); n.number = this.number + n2.number; this.number = n.number; } // n1:7 = n1:3 + n2:4 public static void main(String args[]) { WholeNumber n1 = new WholeNumber(3); WholeNumber n2 = new WholeNumber(4); WholeNumber n1_copy = new WholeNumber(3); n1.add(n2); System.out.printf("n1:%s = n1:%s + n2:%s\n", n1, n1_copy, n2); } } 以上四種寫法也可以合併成一支如下類別,方便比較四種用法的不同: class WholeNumber { public int number; public WholeNumber(int n) { this.number = n; } public WholeNumber(WholeNumber n) { this.number = n.number; } public String toString() { return String.format("%d", number); } public static WholeNumber classAddReturn(WholeNumber n1, WholeNumber n2) { WholeNumber n = new WholeNumber(0); n.number = n1.number + n2.number; return n; } public static void classAddOverwrite(WholeNumber n1, WholeNumber n2) { WholeNumber n = new WholeNumber(0); n.number = n1.number + n2.number; n1.number = n.number; } public WholeNumber instanceAddReturn(WholeNumber n2) { WholeNumber n = new WholeNumber(0); n.number = this.number + n2.number; return n; } public void instanceAddOverwrite(WholeNumber n2) { WholeNumber n = new WholeNumber(0); n.number = this.number + n2.number; this.number = n.number; } /* n3:7 = n1:3 + n2:4 n1:7 = n1:3 + n2:4 n4:7 = n1:3 + n2:4 n1:7 = n1:3 + n2:4 */ public static void main(String args[]) { WholeNumber n1,n2,n3,n4,n1_copy; n1 = new WholeNumber(3); n2 = new WholeNumber(4); n3 = WholeNumber.classAddReturn(n1, n2); System.out.printf("n3:%s = n1:%s + n2:%s\n", n3, n1, n2); n1_copy = new WholeNumber(n1); WholeNumber.classAddOverwrite(n1_copy, n2); System.out.printf("n1:%s = n1:%s + n2:%s\n", n1_copy, n1, n2); n4 = n1.instanceAddReturn(n2); System.out.printf("n4:%s = n1:%s + n2:%s\n", n4, n1, n2); n1_copy = new WholeNumber(n1); n1_copy.instanceAddOverwrite(n2); System.out.printf("n1:%s = n1:%s + n2:%s\n", n1_copy, n1, n2); } }
2018年11月28日 星期三
four design examples for the add function as class or instance methods
Virtual Machine Spec for Google Colab Environment
Google Colab提供最多12小時連線的Jupyter Notebook開發環境,其2018年底層的虛擬機實測規格如下: CPU: Intel(R) Xeon(R) TwinCore @ 2.20GHz x 2 Memory: 13GB Drive: 347GB GPU: Tesla K80 with 4992 cores at 556MHz + 11GB Memory OS: Ubuntu 18.04.1 LTS Time Limit: 12 hours 規格實測的python指令如下: # https://stackoverflow.com/questions/48750199/google-colaboratory-misleading-information-about-its-gpu-only-5-ram-available # memory footprint support libraries/code !ln -sf /opt/bin/nvidia-smi /usr/bin/nvidia-smi !pip install gputil !pip install psutil !pip install humanize import psutil import humanize import os import GPUtil as GPU GPUs = GPU.getGPUs() # XXX: only one GPU on Colab and isn? guaranteed gpu = GPUs[0] def printm(): process = psutil.Process(os.getpid()) print("Gen RAM Free: " + humanize.naturalsize( psutil.virtual_memory().available ), " | Proc size: " + humanize.naturalsize( process.memory_info().rss)) print("GPU RAM Free: {0:.0f}MB | Used: {1:.0f}MB | Util {2:3.0f}% | Total {3:.0f}MB".format(gpu.memoryFree, gpu.memoryUsed, gpu.memoryUtil*100, gpu.memoryTotal)) printm() !df !cat /etc/issue !nvidia-smi !nvidia-smi -L !cat /proc/cpuinfo !cat /proc/meminfo
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