<snapdata remixID="13052103"><project name="U2L3-Predicates" app="Snap! 9.0, https://snap.berkeley.edu" version="2"><notes></notes><thumbnail>data:image/png;base64,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</thumbnail><scenes select="1"><scene name="U2L3-Predicates"><notes></notes><hidden></hidden><headers></headers><code></code><blocks><block-definition s="%&apos;a&apos; ≤ %&apos;b&apos;" type="predicate" category="operators"><header></header><code></code><translations></translations><inputs><input type="%s"></input><input type="%s"></input></inputs><script><block s="doReport"><block s="reportVariadicOr"><list><block s="reportVariadicLessThan"><list><block var="a"/><block var="b"/></list></block><block s="reportVariadicEquals"><list><block var="a"/><block var="b"/></list></block></list></block></block></script></block-definition><block-definition s="%&apos;a&apos; ≥ %&apos;b&apos;" type="predicate" category="operators"><header></header><code></code><translations></translations><inputs><input type="%s"></input><input type="%s"></input></inputs><script><block s="doReport"><block s="reportVariadicOr"><list><block s="reportVariadicGreaterThan"><list><block var="a"/><block var="b"/></list></block><block s="reportVariadicEquals"><list><block var="a"/><block var="b"/></list></block></list></block></block></script></block-definition><block-definition s="%&apos;a&apos; ≠ %&apos;b&apos;" type="predicate" category="operators"><header></header><code></code><translations></translations><inputs><input type="%s"></input><input type="%s"></input></inputs><script><block s="doReport"><block s="reportNot"><block s="reportVariadicEquals"><list><block var="a"/><block var="b"/></list></block></block></block></script></block-definition><block-definition s="is %&apos;mid&apos; between %&apos;low&apos; and %&apos;high&apos; ?" type="predicate" category="operators"><header></header><code></code><translations></translations><inputs><input type="%s"></input><input type="%s"></input><input type="%s"></input></inputs><script><block s="doReport"><block s="reportVariadicAnd"><list><custom-block s="%s ≥ %s"><block var="mid"/><block var="low"/></custom-block><custom-block s="%s ≤ %s"><block var="mid"/><block var="high"/></custom-block></list></block></block></script><scripts><comment x="14.666666666666666" y="88.8" w="304.6666666666667" collapsed="false">As the lab says, it&apos;s okay to use &quot;&gt;&quot; and &quot;&lt;&quot; instead of &quot;≥&quot; and &quot;≤.&quot;</comment></scripts></block-definition><block-definition s="%&apos;p&apos; implies %&apos;q&apos;" type="predicate" category="operators"><header></header><code></code><translations></translations><inputs><input type="%b"></input><input type="%b"></input></inputs><script><block s="doReport"><block s="reportVariadicOr"><list><block s="reportNot"><block var="p"/></block><block var="q"/></list></block></block></script></block-definition><block-definition s="%&apos;p&apos; xor %&apos;q&apos;" type="predicate" category="operators"><header></header><code></code><translations></translations><inputs><input type="%b"></input><input type="%b"></input></inputs><script><block s="doReport"><block s="reportVariadicAnd"><list><block s="reportVariadicOr"><list><block var="p"/><block var="q"/></list></block><block s="reportNot"><block s="reportVariadicAnd"><list><block var="p"/><block var="q"/></list></block></block></list></block></block></script></block-definition></blocks><stage name="Stage" width="480" height="360" costume="0" color="255,255,255,1" tempo="60" threadsafe="false" penlog="false" volume="100" pan="0" lines="round" ternary="false" hyperops="true" codify="false" inheritance="true" sublistIDs="false" 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</pentrails><costumes><list struct="atomic" id="102"></list></costumes><sounds><list struct="atomic" id="103"></list></sounds><variables></variables><blocks></blocks><scripts></scripts><sprites select="1"><sprite name="Sprite" idx="1" x="-778" y="-80" heading="259" scale="1" volume="100" pan="0" rotation="1" draggable="false" costume="0" color="12,0,241,1" pen="tip" id="108"><costumes><list struct="atomic" id="109"></list></costumes><sounds><list struct="atomic" id="110"></list></sounds><blocks></blocks><variables></variables><scripts><comment x="12" y="863.9999999999999" w="401.3333333333333" collapsed="false">TIF A.  i: The and function is TFFF; it reports True only if both inputs are true.&#xD;ii, iii:  The two most meaningful, probably, are shown below. Read their definition to see how operators can be combined to make other operators. </comment><script x="12" y="937.333333333333"><custom-block s="%b implies %b"><l/><l/><comment w="105.33333333333333" collapsed="true">if P then Q. TFTT</comment></custom-block></script><script x="12" y="967.6666666666663"><custom-block s="%b xor %b"><l/><l/><comment w="117.33333333333333" collapsed="true">exclusive OR. FTTF</comment></custom-block></script><comment x="12" y="997.9999999999995" w="402.6666666666667" collapsed="false">TIF iv:  There are sixteen Boolean operators:&#xD;&#xD;TTTT: Constant function, reports True (ignores both inputs)&#xD;TTTF: P OR Q&#xD;TTFT: not-Q implies not-P; P or not-Q&#xD;TTFF: P (ignores second input)&#xD;TFTT: P implies Q&#xD;TFTF: Q (ignores first input)&#xD;TFFT: P = Q (but in the Boolean domain, not general =)&#xD;TFFF: P AND Q&#xD;FTTT: not-(P AND Q), &quot;NAND&quot;&#xD;FTTF: exclusive OR. P ≠ Q&#xD;FTFT: not-Q (ignores first input)&#xD;FTFF: P and not-Q. not(P implies Q)&#xD;FFTT: not-P (ignores second input)&#xD;FFTF: not-P and Q&#xD;FFFT: not-P and not-Q, not(P or Q), &quot;NOR&quot; &#xD;FFFF: Constant function, reports False (ignores both inputs)&#xD;&#xD;That&apos;s all!  No matter how many operators you string together into a complicated expression, it&apos;ll be equivalent to one of these sixteen.  Isn&apos;t that interesting?&#xD;&#xD;If you think of each of the four letters as a binary digit, in the usual convention that True is 1 and False is 0, then these are the four-bit binary numerals (in backwards order; it&apos;s also conventional to start truth tables with True).&#xD;&#xD;Another way to think about this table is to group the four-letter codes by how many Trues they have:&#xD;&#xD;0 or 4: constant function&#xD;1: &quot;and-like&quot; or &quot;nor-like&quot; function (maybe inverting inputs), implication&#xD;3: &quot;or-like&quot;  or &quot;nand-like&quot; function (ditto), nonimplication&#xD;2: function that ignores one input, or =, or ≠.&#xD;&#xD;TIF v:  There are no firm right and wrong answers.  In certain really rare situations, even the constant functions can be useful.  (Think about a higher order function that requires a two-input Boolean operator as its input, and you don&apos;t really want to compute anything with the given inputs...)  But in general, the two constant functions aren&apos;t useful; the four functions that only use one of the two inputs are rarely useful; AND, OR, XOR, and IMPLIES (TFTT) are useful all the time, and the others are useful some of the time.&#xD;&#xD;TIF vi:  For two-input operators, we made four possible sets of inputs.&#xD;For three-input operators, there are eight possible sets (i.e., eight three-bit numerals).&#xD;So a three-input operator is represented as a string of eight letters T or F.&#xD;In other words, each operator is an eight-bit binary numeral.  There are 2^8 = 256 of them.&#xD; </comment><comment x="12" y="1587.3333333333342" w="398" collapsed="false">3.1.  (a) Yes, they all work.&#xD;(b)  Clearly the last two, as one-liners, win on shortness.  The third is slightly shorter.&#xD;(c)  This one really depends on how much experience you&apos;ve had.  If your mind goes blank when you see Boolean operators, then the first one will seem clearest.  But the first one takes more thinking about cases to ensure that each of the three branches is giving the correct result.  Don&apos;t impose one correct answer on this part of the problem; let students say what they think, and have them listen to each other when they differ.&#xD;(d)  I guess this one gives the first two versions a moment in the sun.  What (d) really means, I think, is that the first two don&apos;t use composition of functions.  But that&apos;s one of the fundamental ideas of the course, and if students aren&apos;t yet happy reading compositions, make sure they practice until they /are/ happy with it.&#xD;(e)  The last one clearly wins.  Just consider the name of the function: &quot;greater than or equal to.&quot;  That&apos;s exactly what the fourth version says!&#xD;(f)  Of course there&apos;s no wrong answer; whatever each student thinks right now is that student&apos;s answer.  But you can then raise the question, which would you like to find clearest at the end of the course?</comment><comment x="12" y="1816.6666666666677" w="401.3333333333333" collapsed="false">3.2.  If today is Tuesday, it tells you Mary&apos;s is closed; on other days it&apos;s open.  (Mary Chung&apos;s is a Chinese restaurant near MIT with a long history as a hacker favorite.  It&apos;s known for extra spicy food, and especially for Suan La Chow Show (https://en.wikipedia.org/wiki/Suanla_chaoshou).  It is indeed closed Tuesdays.)&#xD;&#xD;3.3  Our solution to 2.5 already uses nested conditionals. </comment><script x="30" y="37.66666666666629"><block s="receiveGo"></block><block s="clear"></block><block s="down"></block><block s="setSize"><l>3</l></block><block s="doForever"><script><block s="doIfElse"><block s="reportVariadicGreaterThan"><list><block s="yPosition"></block><l>50</l></list></block><script><block s="setColor"><color>255,35,24,1</color></block></script><script><block s="doIfElse"><custom-block s="is %s between %s and %s ?"><block s="yPosition"></block><l>-50</l><l>50</l></custom-block><script><block s="setColor"><color>99,69,0,1</color></block></script><script><block s="setColor"><color>12,0,241,1</color></block></script></block></script></block><block s="doIfElse"><block s="reportMouseDown"></block><script><block s="down"></block></script><script><block s="up"></block></script></block><block s="doGotoObject"><l><option>mouse-pointer</option></l></block></script></block></script></scripts></sprite></sprites></stage><variables></variables></scene></scenes></project><media name="U2L3-Predicates" app="Snap! 9.0, https://snap.berkeley.edu" version="2"></media></snapdata>