1. 367.241g =?10 = ?2 = ?16' 2. 5E9. F516 =?10 = ?2 = ?g 3. 93.578125,10 = ?2 = ?g = ?16 4. 110111001.011110012 = ?10 = ?8= ?16

How can you make your code more suited to the present moment?

The binary addition of 1 + 1 + 1 + 1 = __________.   0100 (base 2)   1111 (base 2)   0001 (base 2)   1001 (base 2)   The binary addition of 1 + 11 + 11 + 11 = __________   1110   1010   1100   1011

Having the following rdd that contains:('37.1242586781', '-119.424203758')('37.7374074392', '-121.790061298')('45.0929780547', '-117.473248072')('34.9786739919', '-111.700983823')('35.8736386531', '-120.421476019')('34.0912863966', '-118.144076285')('35.6720438532', '-120.416438975')('36.284116521', '-119.030325609')('44.4092246446', '-122.891970809')('38.4770167448', '-122.296973442')('38.4296857989', '-121.418135072')('37.3822266494', '-122.016158235') Implement an iterative algorithm (k-means) in Spark to calculate k-means fora set of points that are in a file(rdd), a k-means algorithm in python. Do not use use K-means in MLib of Spark to solve the problem. Follow this pattern: 1. Choose k = 5 random points as starting centers2. Find all points closest to each center (use groupByKey or reduceByKey)3. Find the new center (mean) of each cluster4. If the centers changed by more than convergeDist (e.g.convergeDist = 0.1), iterate again starting from step 2;otherwise, terminate For…

Expression 5+0.001n³ +0.025n 500n +100n¹.5 + 50n log10 n 0.3n+ 5n¹.5 +2.5.n¹.75 n² log₂ n + n(log2₂ n)² n log3 n + n log2 n 3 logg n + log₂ log2 log₂ n 100n +0.01n² 0.01n + 100n² 2n + n0.5 +0.5n1.25 0.01n log₂n + n(log₂ n)² 3 100n log3 n + n³ + 100n 0.003 logan + log₂ log₂ n Dominant term(s) O(...)