Exponential Distribution Random Numbers out of range












0












$begingroup$


Hi All I have following exponential distribution equation to generate different values for random variable 'r':



$$p_r = frac{(1 - alpha)alpha^{CW}}{1 - alpha^{CW}} alpha^{-r}
text{ for } r = 1, dots, CW$$



I have applied inverse sampling to get values of $r$ by equating this equation to random variable $U$ between 0 and 1 and then applying log on both sides. CW is fixed as 10 and $alpha$ as 0.8 . But I get values of
$r$ greater than CW (its max range) and values are positive and negative.






probability-distributions random-variables







share|cite|improve this question











$endgroup$

















    0












    $begingroup$


    Hi All I have following exponential distribution equation to generate different values for random variable 'r':



    $$p_r = frac{(1 - alpha)alpha^{CW}}{1 - alpha^{CW}} alpha^{-r}
    text{ for } r = 1, dots, CW$$



    I have applied inverse sampling to get values of $r$ by equating this equation to random variable $U$ between 0 and 1 and then applying log on both sides. CW is fixed as 10 and $alpha$ as 0.8 . But I get values of
    $r$ greater than CW (its max range) and values are positive and negative.






    probability-distributions random-variables







    share|cite|improve this question











    $endgroup$















      0












      0








      0





      $begingroup$


      Hi All I have following exponential distribution equation to generate different values for random variable 'r':



      $$p_r = frac{(1 - alpha)alpha^{CW}}{1 - alpha^{CW}} alpha^{-r}
      text{ for } r = 1, dots, CW$$



      I have applied inverse sampling to get values of $r$ by equating this equation to random variable $U$ between 0 and 1 and then applying log on both sides. CW is fixed as 10 and $alpha$ as 0.8 . But I get values of
      $r$ greater than CW (its max range) and values are positive and negative.






      probability-distributions random-variables







      share|cite|improve this question











      $endgroup$




      Hi All I have following exponential distribution equation to generate different values for random variable 'r':



      $$p_r = frac{(1 - alpha)alpha^{CW}}{1 - alpha^{CW}} alpha^{-r}
      text{ for } r = 1, dots, CW$$



      I have applied inverse sampling to get values of $r$ by equating this equation to random variable $U$ between 0 and 1 and then applying log on both sides. CW is fixed as 10 and $alpha$ as 0.8 . But I get values of
      $r$ greater than CW (its max range) and values are positive and negative.






      probability-distributions random-variables




      probability-distributions random-variables






      share|cite|improve this question















      share|cite|improve this question













      share|cite|improve this question




      share|cite|improve this question








      edited Dec 4 '18 at 9:38









      Todor Markov

      1,854410




      1,854410










      asked Dec 4 '18 at 8:48









      user3696623user3696623

      12




      12






















          1 Answer
          1






          active

          oldest

          votes


















          0












          $begingroup$

          For convenience, use the shorthand $beta equivfrac{(1 - alpha)alpha^{CW}}{1 - alpha^{CW}}$.



          Generate random variable $U$ uniformly from $[0,1)$, where the observed values are $u$, then the desired random variable $R$ (which observed values are $r$) can be obtained as



          begin{align}
          r &= 1 & &text{if} & 0 le frac{u}{beta} & < alpha^{-1} \
          r &= 2 & &text{if} & alpha^{-1} le frac{u}{beta} & < alpha^{-1} + alpha^{-2} \
          r &= 3 & &text{if} & alpha^{-1} + alpha^{-2} le frac{u}{beta} &< alpha^{-1} + alpha^{-2} + alpha^{-3} \
          r &= 4 & &text{if} & alpha^{-1} + alpha^{-2} + alpha^{-3} le frac{u}{beta} &< alpha^{-1} + alpha^{-2} + alpha^{-3} + alpha^{-4}\
          &vdots &&vdots &&vdots \
          r &= k & &text{if} & sum_{j=1}^{k-1} alpha^{-j} le frac{u}{beta} &< sum_{j=1}^k alpha^{-j} \
          &vdots &&vdots &&vdots \
          r &= CW & &text{if} & sum_{j=1}^{CW-1} alpha^{-j} le frac{u}{beta} &< sum_{j=1}^{CW} alpha^{-j}
          end{align}



          By the way, this is not an exponential distribution. It is a truncated Geometric distribution.



          Your original approach of "applying log on both sides" is the method of inverse transformation that works only on continuous distributions when directly applied like that.






          share|cite|improve this answer









          $endgroup$













          • $begingroup$
            1. Thanks for explanation and sorry for late reply . This equation is from SIFT MAC Protocol research paper [Jamieson, K., Balakrishnan, H., & Tay, Y. C. (2006, February). Sift: A MAC protocol for event-driven wireless sensor networks. In European workshop on wireless sensor networks (pp. 260-275). Springer, Berlin, Heidelberg] and author says that it is exponential. any ways may be i understood wrong. 2. How can we make this equation more compact as i need to program it ?
            $endgroup$
            – user3696623
            Dec 22 '18 at 12:20












          • $begingroup$
            (1) People from different fields use different terminology. If no such communication problem is encountered in your line of work then it's not my place to judge. Just FYI in many communities, Geometric Distribution and Exponential Distribution refer to two different things that are mutual analogues, with the former being discrete and latter continuous.
            $endgroup$
            – Lee David Chung Lin
            Dec 23 '18 at 10:39










          • $begingroup$
            (2) This set of equations can be easily vectorized (coded compactly) in most commonly used programming languages or Matlab, R, Mathematica, etc. What platform are you working with?
            $endgroup$
            – Lee David Chung Lin
            Dec 23 '18 at 10:40










          • $begingroup$
            I am working in C++.
            $endgroup$
            – user3696623
            Dec 24 '18 at 11:46










          • $begingroup$
            Continue the two bullets above (1) I finally take a look at the (various versions) of the paper by Jamieson and Balakrishnan. They clearly stated that "Sift uses a truncated increasing geometric distribution". This and that are two highlighted snapshots. I don't know why you said they call it exponential. I advise you edit the question title, otherwise the suggested links are all irrelevant.
            $endgroup$
            – Lee David Chung Lin
            Dec 25 '18 at 11:50













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          1 Answer
          1






          active

          oldest

          votes









          active

          oldest

          votes






          active

          oldest

          votes









          0












          $begingroup$

          For convenience, use the shorthand $beta equivfrac{(1 - alpha)alpha^{CW}}{1 - alpha^{CW}}$.



          Generate random variable $U$ uniformly from $[0,1)$, where the observed values are $u$, then the desired random variable $R$ (which observed values are $r$) can be obtained as



          begin{align}
          r &= 1 & &text{if} & 0 le frac{u}{beta} & < alpha^{-1} \
          r &= 2 & &text{if} & alpha^{-1} le frac{u}{beta} & < alpha^{-1} + alpha^{-2} \
          r &= 3 & &text{if} & alpha^{-1} + alpha^{-2} le frac{u}{beta} &< alpha^{-1} + alpha^{-2} + alpha^{-3} \
          r &= 4 & &text{if} & alpha^{-1} + alpha^{-2} + alpha^{-3} le frac{u}{beta} &< alpha^{-1} + alpha^{-2} + alpha^{-3} + alpha^{-4}\
          &vdots &&vdots &&vdots \
          r &= k & &text{if} & sum_{j=1}^{k-1} alpha^{-j} le frac{u}{beta} &< sum_{j=1}^k alpha^{-j} \
          &vdots &&vdots &&vdots \
          r &= CW & &text{if} & sum_{j=1}^{CW-1} alpha^{-j} le frac{u}{beta} &< sum_{j=1}^{CW} alpha^{-j}
          end{align}



          By the way, this is not an exponential distribution. It is a truncated Geometric distribution.



          Your original approach of "applying log on both sides" is the method of inverse transformation that works only on continuous distributions when directly applied like that.






          share|cite|improve this answer









          $endgroup$













          • $begingroup$
            1. Thanks for explanation and sorry for late reply . This equation is from SIFT MAC Protocol research paper [Jamieson, K., Balakrishnan, H., & Tay, Y. C. (2006, February). Sift: A MAC protocol for event-driven wireless sensor networks. In European workshop on wireless sensor networks (pp. 260-275). Springer, Berlin, Heidelberg] and author says that it is exponential. any ways may be i understood wrong. 2. How can we make this equation more compact as i need to program it ?
            $endgroup$
            – user3696623
            Dec 22 '18 at 12:20












          • $begingroup$
            (1) People from different fields use different terminology. If no such communication problem is encountered in your line of work then it's not my place to judge. Just FYI in many communities, Geometric Distribution and Exponential Distribution refer to two different things that are mutual analogues, with the former being discrete and latter continuous.
            $endgroup$
            – Lee David Chung Lin
            Dec 23 '18 at 10:39










          • $begingroup$
            (2) This set of equations can be easily vectorized (coded compactly) in most commonly used programming languages or Matlab, R, Mathematica, etc. What platform are you working with?
            $endgroup$
            – Lee David Chung Lin
            Dec 23 '18 at 10:40










          • $begingroup$
            I am working in C++.
            $endgroup$
            – user3696623
            Dec 24 '18 at 11:46










          • $begingroup$
            Continue the two bullets above (1) I finally take a look at the (various versions) of the paper by Jamieson and Balakrishnan. They clearly stated that "Sift uses a truncated increasing geometric distribution". This and that are two highlighted snapshots. I don't know why you said they call it exponential. I advise you edit the question title, otherwise the suggested links are all irrelevant.
            $endgroup$
            – Lee David Chung Lin
            Dec 25 '18 at 11:50


















          0












          $begingroup$

          For convenience, use the shorthand $beta equivfrac{(1 - alpha)alpha^{CW}}{1 - alpha^{CW}}$.



          Generate random variable $U$ uniformly from $[0,1)$, where the observed values are $u$, then the desired random variable $R$ (which observed values are $r$) can be obtained as



          begin{align}
          r &= 1 & &text{if} & 0 le frac{u}{beta} & < alpha^{-1} \
          r &= 2 & &text{if} & alpha^{-1} le frac{u}{beta} & < alpha^{-1} + alpha^{-2} \
          r &= 3 & &text{if} & alpha^{-1} + alpha^{-2} le frac{u}{beta} &< alpha^{-1} + alpha^{-2} + alpha^{-3} \
          r &= 4 & &text{if} & alpha^{-1} + alpha^{-2} + alpha^{-3} le frac{u}{beta} &< alpha^{-1} + alpha^{-2} + alpha^{-3} + alpha^{-4}\
          &vdots &&vdots &&vdots \
          r &= k & &text{if} & sum_{j=1}^{k-1} alpha^{-j} le frac{u}{beta} &< sum_{j=1}^k alpha^{-j} \
          &vdots &&vdots &&vdots \
          r &= CW & &text{if} & sum_{j=1}^{CW-1} alpha^{-j} le frac{u}{beta} &< sum_{j=1}^{CW} alpha^{-j}
          end{align}



          By the way, this is not an exponential distribution. It is a truncated Geometric distribution.



          Your original approach of "applying log on both sides" is the method of inverse transformation that works only on continuous distributions when directly applied like that.






          share|cite|improve this answer









          $endgroup$













          • $begingroup$
            1. Thanks for explanation and sorry for late reply . This equation is from SIFT MAC Protocol research paper [Jamieson, K., Balakrishnan, H., & Tay, Y. C. (2006, February). Sift: A MAC protocol for event-driven wireless sensor networks. In European workshop on wireless sensor networks (pp. 260-275). Springer, Berlin, Heidelberg] and author says that it is exponential. any ways may be i understood wrong. 2. How can we make this equation more compact as i need to program it ?
            $endgroup$
            – user3696623
            Dec 22 '18 at 12:20












          • $begingroup$
            (1) People from different fields use different terminology. If no such communication problem is encountered in your line of work then it's not my place to judge. Just FYI in many communities, Geometric Distribution and Exponential Distribution refer to two different things that are mutual analogues, with the former being discrete and latter continuous.
            $endgroup$
            – Lee David Chung Lin
            Dec 23 '18 at 10:39










          • $begingroup$
            (2) This set of equations can be easily vectorized (coded compactly) in most commonly used programming languages or Matlab, R, Mathematica, etc. What platform are you working with?
            $endgroup$
            – Lee David Chung Lin
            Dec 23 '18 at 10:40










          • $begingroup$
            I am working in C++.
            $endgroup$
            – user3696623
            Dec 24 '18 at 11:46










          • $begingroup$
            Continue the two bullets above (1) I finally take a look at the (various versions) of the paper by Jamieson and Balakrishnan. They clearly stated that "Sift uses a truncated increasing geometric distribution". This and that are two highlighted snapshots. I don't know why you said they call it exponential. I advise you edit the question title, otherwise the suggested links are all irrelevant.
            $endgroup$
            – Lee David Chung Lin
            Dec 25 '18 at 11:50
















          0












          0








          0





          $begingroup$

          For convenience, use the shorthand $beta equivfrac{(1 - alpha)alpha^{CW}}{1 - alpha^{CW}}$.



          Generate random variable $U$ uniformly from $[0,1)$, where the observed values are $u$, then the desired random variable $R$ (which observed values are $r$) can be obtained as



          begin{align}
          r &= 1 & &text{if} & 0 le frac{u}{beta} & < alpha^{-1} \
          r &= 2 & &text{if} & alpha^{-1} le frac{u}{beta} & < alpha^{-1} + alpha^{-2} \
          r &= 3 & &text{if} & alpha^{-1} + alpha^{-2} le frac{u}{beta} &< alpha^{-1} + alpha^{-2} + alpha^{-3} \
          r &= 4 & &text{if} & alpha^{-1} + alpha^{-2} + alpha^{-3} le frac{u}{beta} &< alpha^{-1} + alpha^{-2} + alpha^{-3} + alpha^{-4}\
          &vdots &&vdots &&vdots \
          r &= k & &text{if} & sum_{j=1}^{k-1} alpha^{-j} le frac{u}{beta} &< sum_{j=1}^k alpha^{-j} \
          &vdots &&vdots &&vdots \
          r &= CW & &text{if} & sum_{j=1}^{CW-1} alpha^{-j} le frac{u}{beta} &< sum_{j=1}^{CW} alpha^{-j}
          end{align}



          By the way, this is not an exponential distribution. It is a truncated Geometric distribution.



          Your original approach of "applying log on both sides" is the method of inverse transformation that works only on continuous distributions when directly applied like that.






          share|cite|improve this answer









          $endgroup$



          For convenience, use the shorthand $beta equivfrac{(1 - alpha)alpha^{CW}}{1 - alpha^{CW}}$.



          Generate random variable $U$ uniformly from $[0,1)$, where the observed values are $u$, then the desired random variable $R$ (which observed values are $r$) can be obtained as



          begin{align}
          r &= 1 & &text{if} & 0 le frac{u}{beta} & < alpha^{-1} \
          r &= 2 & &text{if} & alpha^{-1} le frac{u}{beta} & < alpha^{-1} + alpha^{-2} \
          r &= 3 & &text{if} & alpha^{-1} + alpha^{-2} le frac{u}{beta} &< alpha^{-1} + alpha^{-2} + alpha^{-3} \
          r &= 4 & &text{if} & alpha^{-1} + alpha^{-2} + alpha^{-3} le frac{u}{beta} &< alpha^{-1} + alpha^{-2} + alpha^{-3} + alpha^{-4}\
          &vdots &&vdots &&vdots \
          r &= k & &text{if} & sum_{j=1}^{k-1} alpha^{-j} le frac{u}{beta} &< sum_{j=1}^k alpha^{-j} \
          &vdots &&vdots &&vdots \
          r &= CW & &text{if} & sum_{j=1}^{CW-1} alpha^{-j} le frac{u}{beta} &< sum_{j=1}^{CW} alpha^{-j}
          end{align}



          By the way, this is not an exponential distribution. It is a truncated Geometric distribution.



          Your original approach of "applying log on both sides" is the method of inverse transformation that works only on continuous distributions when directly applied like that.







          share|cite|improve this answer












          share|cite|improve this answer



          share|cite|improve this answer










          answered Dec 4 '18 at 13:31









          Lee David Chung LinLee David Chung Lin

          3,98031140




          3,98031140












          • $begingroup$
            1. Thanks for explanation and sorry for late reply . This equation is from SIFT MAC Protocol research paper [Jamieson, K., Balakrishnan, H., & Tay, Y. C. (2006, February). Sift: A MAC protocol for event-driven wireless sensor networks. In European workshop on wireless sensor networks (pp. 260-275). Springer, Berlin, Heidelberg] and author says that it is exponential. any ways may be i understood wrong. 2. How can we make this equation more compact as i need to program it ?
            $endgroup$
            – user3696623
            Dec 22 '18 at 12:20












          • $begingroup$
            (1) People from different fields use different terminology. If no such communication problem is encountered in your line of work then it's not my place to judge. Just FYI in many communities, Geometric Distribution and Exponential Distribution refer to two different things that are mutual analogues, with the former being discrete and latter continuous.
            $endgroup$
            – Lee David Chung Lin
            Dec 23 '18 at 10:39










          • $begingroup$
            (2) This set of equations can be easily vectorized (coded compactly) in most commonly used programming languages or Matlab, R, Mathematica, etc. What platform are you working with?
            $endgroup$
            – Lee David Chung Lin
            Dec 23 '18 at 10:40










          • $begingroup$
            I am working in C++.
            $endgroup$
            – user3696623
            Dec 24 '18 at 11:46










          • $begingroup$
            Continue the two bullets above (1) I finally take a look at the (various versions) of the paper by Jamieson and Balakrishnan. They clearly stated that "Sift uses a truncated increasing geometric distribution". This and that are two highlighted snapshots. I don't know why you said they call it exponential. I advise you edit the question title, otherwise the suggested links are all irrelevant.
            $endgroup$
            – Lee David Chung Lin
            Dec 25 '18 at 11:50




















          • $begingroup$
            1. Thanks for explanation and sorry for late reply . This equation is from SIFT MAC Protocol research paper [Jamieson, K., Balakrishnan, H., & Tay, Y. C. (2006, February). Sift: A MAC protocol for event-driven wireless sensor networks. In European workshop on wireless sensor networks (pp. 260-275). Springer, Berlin, Heidelberg] and author says that it is exponential. any ways may be i understood wrong. 2. How can we make this equation more compact as i need to program it ?
            $endgroup$
            – user3696623
            Dec 22 '18 at 12:20












          • $begingroup$
            (1) People from different fields use different terminology. If no such communication problem is encountered in your line of work then it's not my place to judge. Just FYI in many communities, Geometric Distribution and Exponential Distribution refer to two different things that are mutual analogues, with the former being discrete and latter continuous.
            $endgroup$
            – Lee David Chung Lin
            Dec 23 '18 at 10:39










          • $begingroup$
            (2) This set of equations can be easily vectorized (coded compactly) in most commonly used programming languages or Matlab, R, Mathematica, etc. What platform are you working with?
            $endgroup$
            – Lee David Chung Lin
            Dec 23 '18 at 10:40










          • $begingroup$
            I am working in C++.
            $endgroup$
            – user3696623
            Dec 24 '18 at 11:46










          • $begingroup$
            Continue the two bullets above (1) I finally take a look at the (various versions) of the paper by Jamieson and Balakrishnan. They clearly stated that "Sift uses a truncated increasing geometric distribution". This and that are two highlighted snapshots. I don't know why you said they call it exponential. I advise you edit the question title, otherwise the suggested links are all irrelevant.
            $endgroup$
            – Lee David Chung Lin
            Dec 25 '18 at 11:50


















          $begingroup$
          1. Thanks for explanation and sorry for late reply . This equation is from SIFT MAC Protocol research paper [Jamieson, K., Balakrishnan, H., & Tay, Y. C. (2006, February). Sift: A MAC protocol for event-driven wireless sensor networks. In European workshop on wireless sensor networks (pp. 260-275). Springer, Berlin, Heidelberg] and author says that it is exponential. any ways may be i understood wrong. 2. How can we make this equation more compact as i need to program it ?
          $endgroup$
          – user3696623
          Dec 22 '18 at 12:20






          $begingroup$
          1. Thanks for explanation and sorry for late reply . This equation is from SIFT MAC Protocol research paper [Jamieson, K., Balakrishnan, H., & Tay, Y. C. (2006, February). Sift: A MAC protocol for event-driven wireless sensor networks. In European workshop on wireless sensor networks (pp. 260-275). Springer, Berlin, Heidelberg] and author says that it is exponential. any ways may be i understood wrong. 2. How can we make this equation more compact as i need to program it ?
          $endgroup$
          – user3696623
          Dec 22 '18 at 12:20














          $begingroup$
          (1) People from different fields use different terminology. If no such communication problem is encountered in your line of work then it's not my place to judge. Just FYI in many communities, Geometric Distribution and Exponential Distribution refer to two different things that are mutual analogues, with the former being discrete and latter continuous.
          $endgroup$
          – Lee David Chung Lin
          Dec 23 '18 at 10:39




          $begingroup$
          (1) People from different fields use different terminology. If no such communication problem is encountered in your line of work then it's not my place to judge. Just FYI in many communities, Geometric Distribution and Exponential Distribution refer to two different things that are mutual analogues, with the former being discrete and latter continuous.
          $endgroup$
          – Lee David Chung Lin
          Dec 23 '18 at 10:39












          $begingroup$
          (2) This set of equations can be easily vectorized (coded compactly) in most commonly used programming languages or Matlab, R, Mathematica, etc. What platform are you working with?
          $endgroup$
          – Lee David Chung Lin
          Dec 23 '18 at 10:40




          $begingroup$
          (2) This set of equations can be easily vectorized (coded compactly) in most commonly used programming languages or Matlab, R, Mathematica, etc. What platform are you working with?
          $endgroup$
          – Lee David Chung Lin
          Dec 23 '18 at 10:40












          $begingroup$
          I am working in C++.
          $endgroup$
          – user3696623
          Dec 24 '18 at 11:46




          $begingroup$
          I am working in C++.
          $endgroup$
          – user3696623
          Dec 24 '18 at 11:46












          $begingroup$
          Continue the two bullets above (1) I finally take a look at the (various versions) of the paper by Jamieson and Balakrishnan. They clearly stated that "Sift uses a truncated increasing geometric distribution". This and that are two highlighted snapshots. I don't know why you said they call it exponential. I advise you edit the question title, otherwise the suggested links are all irrelevant.
          $endgroup$
          – Lee David Chung Lin
          Dec 25 '18 at 11:50






          $begingroup$
          Continue the two bullets above (1) I finally take a look at the (various versions) of the paper by Jamieson and Balakrishnan. They clearly stated that "Sift uses a truncated increasing geometric distribution". This and that are two highlighted snapshots. I don't know why you said they call it exponential. I advise you edit the question title, otherwise the suggested links are all irrelevant.
          $endgroup$
          – Lee David Chung Lin
          Dec 25 '18 at 11:50




















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          Le Mesnil-Réaume

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          Le Mesnil-Réaume Region Normandie Département Seine-Maritime Arrondissement Dieppe Kanton Eu Gemeindeverband Villes Sœurs Koordinaten 49° 58′  N , 1° 27′  O 49.9725 1.4463888888889 Koordinaten: 49° 58′  N , 1° 27′  O Höhe 92–156 m Fläche 5,55 km 2 Einwohner 793 (1. Januar 2016) Bevölkerungsdichte 143 Einw./km 2 Postleitzahl 76260 INSEE-Code 76435 Website http://www.mesnil-reaume.net/ Rathaus (Mairie) und Schule von Le Mesnil-Réaume Le Mesnil-Réaume ist eine französische Gemeinde mit 793 Einwohnern (Stand: 1. Januar 2016) im Département Seine-Maritime in der Region Normandie (vor 2016 Haute-Normandie). Sie gehört zum Arrondissement Dieppe und zum Kanton Eu. Die Einwohner werden Mesnillais genannt. Inhaltsverzeichnis 1 Geographie 2 Bevölkerungsentwicklung 3 Sehenswürdigkeiten 4 Weblinks Geographie | Le Mesnil-Réaume liegt etwa 43 Kilomet
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          Ida-Boy-Ed-Garten

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          Die Lage des Ida-Boy-Ed-Gartens, rot markiert Der Ida-Boy-Ed-Garten Der Ida-Boy-Ed-Garten, Blick von der Wakenitzmauer durch das Tor in der Stadtmauer Der Ida-Boy-Ed-Garten ist eine Straße der Lübecker Altstadt. Inhaltsverzeichnis 1 Lage 2 Geschichte 3 Literatur 4 Weblinks Lage | Der etwa 90 Meter lange Ida-Boy-Ed-Garten befindet sich an der nördlichen Spitze der Altstadtinsel, vor dem Burgtor. Die in einem Bogen durch eine Grünanlage hangaufwärts führende Straße verbindet die Wakenitzmauer mit der Burgtorbrücke. Geschichte | Der heutige Ida-Boy-Ed-Garten wurde ursprünglich als Verlängerung der Wakenitzmauer angelegt. 1903 wurde in den erhaltenen Teil der mittelalterlichen Stadtmauer, an dem die Wakenitzmauer bei der Einmündung der Kaiserstraße begann, ein Tor mit einem großen Bogen für die Durchführung der Straße und zwei kleineren seitlichen Öffnungen für Fußgänger gebrochen, um für die neu errichteten Wohnhäuser in diesem Bereich
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          web3.py web3.isConnected() returns false always

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          1 I am trying an tutorial on web3.py so i created a profile in infura and used its project id.But it shows false always when i try web3.isConnected() PS C:UsersHP> python Python 3.6.3 (v3.6.3:2c5fed8, Oct 3 2017, 18:11:49) [MSC v.1900 64 bit (AMD64)] on win32 Type "help", "copyright", "credits" or "license" for more information. >>> from web3 import Web3 >>> infura_url = "mainnet.infura.io/v3/******************************" >>> web3 = Web3(Web3.HTTPProvider(infura_url)) >>> web3.isConnected() False web3.py share | improve this question
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