Input Value Contains Quotes

1.1M    ./scripts 58M     ./cloud9 74M     . You can also use tee to write the output to several files at the same time, as shown in this example: root@beaglebone:/opt# du ‐d1 ‐h | tee /tmp/1.txt /tmp/2.txt /tmp/3.txt Filter Commands (from sort to xargs) There are filtering commands, each of which provides a useful function: sort: This command has several options, including (‐r) sorts in reverse; (‐f) ignores case; (‐d) uses dictionary sorting, ignoring punctuation; (‐n) numeric sort; (‐b) ignores blank space; (‐i) ignores control characters; (‐u) displays duplicate lines only once; and (‐m) merges multiple inputs into a single output. wc (word count): This can be used to calculate the number of words, lines, or characters in a stream. For example: root@beaglebone:/tmp# wc < animals.txt  4  4 18 This has returned that there are 4 lines, 4 words, and 18 characters. You can select the values independently by using (‐l) for line count; (‐w) for word count; (‐m) for character count; and (‐c) for the byte count (which would also be 18 in this case). head: Displays the first lines of the input. This is useful if you have a very long file or stream of information and you want to examine only the first few lines. By default it will display the first 10 lines. You can specify the number of lines using the ‐n option. For example, to get the first five lines of output of the dmesg command (display message or driver message), which displays the message buffer of the kernel, you can use the following: root@beaglebone:/tmp# dmesg | head ‐n5   [    0.000000] Booting Linux on physical CPU 0x0   [    0.000000] Initializing cgroup subsys cpuset   [    0.000000] Initializing cgroup subsys cpu   [    0.000000] Initializing cgroup subsys cpuacct   [    0.000000] Linux version 3.13.4-bone5(root@imx6q-sabrelite-1gb-0) tail: This is just like head except that it displays the last lines of a file or stream. Using it in combination with dmesg provides useful output, as shown here: root@beaglebone:/tmp# dmesg | tail ‐n2   [   36.123251] libphy: 4a101000.mdio:00 - Link is Up - 100/Full   [   36.123421] IPv6:ADDRCONF(NETDEV_CHANGE): eth0:link becomes ready grep: A very powerful filter command that can parse lines using text and regular expressions. You can use this command to filter output with options, including (‐i) ignore case; (‐m 5) stop after five matches; (‐q) silent, will exit with return status 0 if any matches are found; (‐e) specify a pattern; (‐c) print a count of matches; (‐o) print only the matching text; and (‐l) list the filename of the file containing the match. For example, the following examines the dmesg output for the first three occurrences of the string “usb,” using ‐i to ignore case: root@beaglebone:/tmp# dmesg |grep ‐i ‐m3 usb   [    1.948582] usbcore: registered new interface driver usbfs   [    1.948637] usbcore: registered new interface driver hub   [    1.948795] usbcore: registered new device driver usb You can combine pipes together. For example, you get the exact same output by using head and displaying only the first three lines of the grep output: root@beaglebone:/tmp# dmesg |grep ‐i usb |head ‐n3   [    1.948582] usbcore: registered new interface driver usbfs   [    1.948637] usbcore: registered new interface driver hub   [    1.948795] usbcore: registered new device driver usb xargs: This is a very powerful filter command that enables you to construct an argument list that you use to call another command or tool. In the following example, a text file args.txt that contains three strings is used to create three new files. The output of cat is piped to xargs, where it passes the three strings as arguments to the touch command, creating three new files a.txt, b.txt,

Let us look at the correlation between temperature, humidity and wind speed and all other features. Since the data also contains categorical features, we cannot only use the Pearson correlation coefficient, which only works if both features are numerical. Instead, I train a linear model to predict, for example, temperature based on one of the other features as input. Then I measure how much variance the other feature in the linear model explains and take the square root. If the other feature was numerical, then the result is equal to the absolute value of the standard Pearson correlation coefficient. But this model-based approach of “variance-explained” (also called ANOVA, which stands for ANalysis Of VAriance) works even if the other feature is categorical. The “variance-explained” measure lies always between 0 (no association) and 1 (temperature can be perfectly predicted from the other feature). We calculate the explained variance of temperature, humidity and wind speed with all the other features. The higher the explained variance (correlation), the more (potential) problems with PD plots. The following figure visualizes how strongly the weather features are correlated with other features.

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